diff --git a/clustering/.ipynb_checkpoints/cluster_GWR-checkpoint.ipynb b/clustering/.ipynb_checkpoints/cluster_GWR-checkpoint.ipynb new file mode 100644 index 0000000..1930529 --- /dev/null +++ b/clustering/.ipynb_checkpoints/cluster_GWR-checkpoint.ipynb @@ -0,0 +1,4140 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import xarray as xr\n", + "\n", + "from sklearn.cluster import DBSCAN\n", + "from sklearn.cluster import KMeans\n", + "\n", + "import os\n", + "import time\n", + "import calendar\n", + "\n", + "%matplotlib notebook\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def categorical_cmap(df, column, cmap = 'tab20', N = 20):\n", + " # Get colormap data\n", + " c = matplotlib.cm.get_cmap( cmap )(np.linspace(0., 1, N))\n", + " \n", + " # Get length of series, number of total copies and remainder\n", + " l = len(df[ column ].unique())\n", + " n = int(np.floor(l / N))\n", + " r = l % N\n", + " \n", + " # Concatenate colormap\n", + " clist = [c]*n\n", + " clist.append( c[:r,:] )\n", + " colors = np.vstack(clist)\n", + " \n", + " # return colormap\n", + " return matplotlib.colors.LinearSegmentedColormap.from_list('%s_concat' %cmap, colors)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def get_cluster_demand(BLD_df, demand, grouping_col, uniqueID_col = 'EGID',\n", + " dwell_nb = 'N_DWELL', canton_id = 'GDEKT', \n", + " SFH_demand = 'maison', MFH_demand = 'appart', \n", + " communal_demand = 'communal_rest', communal_demand_GE = 'communal_GVA'):\n", + "\n", + " SFH = BLD_df[BLD_df[dwell_nb] == 1]\n", + " MFH = BLD_df[BLD_df[dwell_nb] > 1]\n", + " \n", + " building_counts = xr.Dataset({ 'N_SFH' : SFH.groupby( grouping_col )[ uniqueID_col ].count(),\n", + " 'N_DWELL' : MFH.groupby( grouping_col )[ dwell_nb ].sum(),\n", + " 'N_MFH' : MFH[MFH.GDEKT != 'GE'].groupby( grouping_col )[ uniqueID_col ].count(),\n", + " 'N_MFH_GE': MFH[MFH.GDEKT == 'GE'].groupby( grouping_col )[ uniqueID_col ].count() }\n", + " ).fillna(0)\n", + "\n", + " cluster_demand = ( building_counts['N_SFH'] * demand[SFH_demand] \n", + " + building_counts['N_DWELL'] * demand[MFH_demand]\n", + " + building_counts['N_MFH'] * demand[communal_demand] \n", + " + building_counts['N_MFH_GE']* demand[communal_demand_GE] )\n", + "\n", + " return [cluster_demand, building_counts]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load GWR" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "GWR_fp = 'D:\\Building_data\\OFS\\Buildings\\GWR_MADD_Export_MADD-20200715-A3_20200722'\n", + "GWR = pd.read_csv(os.path.join(GWR_fp, 'GWR_GEB_w_N_WHG.csv'))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "coords = ['GKODE', 'GKODN']\n", + "GWR = GWR.dropna(subset = coords) # can only cluster GWR entries with coordinate information" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " EGID GKODE GKODN GSTAT GKAT GKLAS GGDENR GDEKT \\\n", + "0 1600000 2679684.326 1237449.553 1004.0 1020.0 1122.0 1 ZH \n", + "1 1600001 2679702.150 1237493.958 1004.0 1030.0 1110.0 1 ZH \n", + "2 1600002 2679713.052 1237475.814 1004.0 1030.0 1121.0 1 ZH \n", + "3 1600003 2679655.455 1237516.450 1004.0 1020.0 1122.0 1 ZH \n", + "4 1600005 2679749.269 1237529.346 1004.0 1020.0 1110.0 1 ZH \n", + "\n", + " N_DWELL \n", + "0 3.0 \n", + "1 1.0 \n", + "2 2.0 \n", + "3 3.0 \n", + "4 1.0 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "GWR.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Extract only Canton GE for testing\n", + "# GWR = GWR[GWR.GDEKT == 'GE']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load electricity demand" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "demand_fp = 'D:\\Demand' # '/work/hyenergy/output/demand'\n", + "residential_demand = xr.open_dataset(os.path.join(demand_fp, 'electricity_demand_residential.nc')) " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset>\n",
+       "Dimensions:        (daytype: 2, timestamp: 288)\n",
+       "Coordinates:\n",
+       "  * timestamp      (timestamp) datetime64[ns] 2001-01-15 ... 2001-12-15T23:00:00\n",
+       "  * daytype        (daytype) object 'weekday' 'weekend'\n",
+       "Data variables:\n",
+       "    appart         (timestamp, daytype) float64 162.3 181.7 ... 233.7 227.8\n",
+       "    maison         (timestamp, daytype) float64 298.2 344.7 ... 377.5 411.3\n",
+       "    communal_GVA   (timestamp, daytype) float64 241.3 557.0 ... 121.0 302.5\n",
+       "    communal_rest  (timestamp, daytype) float64 79.66 183.9 75.14 ... 39.96 99.9\n",
+       "    weight         (timestamp, daytype) float64 0.0001138 ... 0.0001427\n",
+       "    GVA_annual     float64 2.12e+06\n",
+       "    rest_annual    float64 7e+05
" + ], + "text/plain": [ + "\n", + "Dimensions: (daytype: 2, timestamp: 288)\n", + "Coordinates:\n", + " * timestamp (timestamp) datetime64[ns] 2001-01-15 ... 2001-12-15T23:00:00\n", + " * daytype (daytype) object 'weekday' 'weekend'\n", + "Data variables:\n", + " appart (timestamp, daytype) float64 ...\n", + " maison (timestamp, daytype) float64 ...\n", + " communal_GVA (timestamp, daytype) float64 ...\n", + " communal_rest (timestamp, daytype) float64 ...\n", + " weight (timestamp, daytype) float64 ...\n", + " GVA_annual float64 ...\n", + " rest_annual float64 ..." + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "residential_demand" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Perform DBSCAN" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "EPS = 250\n", + "MIN_SAMPLES = 10\n", + "\n", + "model = DBSCAN(eps = EPS, min_samples = MIN_SAMPLES, n_jobs = -1)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit and predict in 13.52s\n" + ] + } + ], + "source": [ + "tt = time.time()\n", + "GWR['clusterID'] = model.fit_predict( GWR[coords] )\n", + "print('Fit and predict in %.2fs' %(time.time()-tt))" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support. ' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('
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');\n", + "\n", + " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", + "\n", + " function canvas_keyboard_event(event) {\n", + " return fig.key_event(event, event['data']);\n", + " }\n", + "\n", + " canvas_div.keydown('key_press', canvas_keyboard_event);\n", + " canvas_div.keyup('key_release', canvas_keyboard_event);\n", + " this.canvas_div = canvas_div\n", + " this._canvas_extra_style(canvas_div)\n", + " this.root.append(canvas_div);\n", + "\n", + " var canvas = $('');\n", + " canvas.addClass('mpl-canvas');\n", + " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", + "\n", + " this.canvas = canvas[0];\n", + " this.context = canvas[0].getContext(\"2d\");\n", + "\n", + " var backingStore = this.context.backingStorePixelRatio ||\n", + "\tthis.context.webkitBackingStorePixelRatio ||\n", + "\tthis.context.mozBackingStorePixelRatio ||\n", + "\tthis.context.msBackingStorePixelRatio ||\n", + "\tthis.context.oBackingStorePixelRatio ||\n", + "\tthis.context.backingStorePixelRatio || 1;\n", + "\n", + " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband = $('');\n", + " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", + "\n", + " var pass_mouse_events = true;\n", + "\n", + " canvas_div.resizable({\n", + " start: function(event, ui) {\n", + " pass_mouse_events = false;\n", + " },\n", + " resize: function(event, ui) {\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " stop: function(event, ui) {\n", + " pass_mouse_events = true;\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " });\n", + "\n", + " function mouse_event_fn(event) {\n", + " if (pass_mouse_events)\n", + " return fig.mouse_event(event, event['data']);\n", + " }\n", + "\n", + " rubberband.mousedown('button_press', mouse_event_fn);\n", + " rubberband.mouseup('button_release', mouse_event_fn);\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband.mousemove('motion_notify', mouse_event_fn);\n", + "\n", + " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", + " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", + "\n", + " canvas_div.on(\"wheel\", function (event) {\n", + " event = event.originalEvent;\n", + " event['data'] = 'scroll'\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " mouse_event_fn(event);\n", + " });\n", + "\n", + " canvas_div.append(canvas);\n", + " canvas_div.append(rubberband);\n", + "\n", + " this.rubberband = rubberband;\n", + " this.rubberband_canvas = rubberband[0];\n", + " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", + " this.rubberband_context.strokeStyle = \"#000000\";\n", + "\n", + " this._resize_canvas = function(width, height) {\n", + " // Keep the size of the canvas, canvas container, and rubber band\n", + " // canvas in synch.\n", + " canvas_div.css('width', width)\n", + " canvas_div.css('height', height)\n", + "\n", + " canvas.attr('width', width * mpl.ratio);\n", + " canvas.attr('height', height * mpl.ratio);\n", + " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", + "\n", + " rubberband.attr('width', width);\n", + " rubberband.attr('height', height);\n", + " }\n", + "\n", + " // Set the figure to an initial 600x600px, this will subsequently be updated\n", + " // upon first draw.\n", + " this._resize_canvas(600, 600);\n", + "\n", + " // Disable right mouse context menu.\n", + " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", + " return false;\n", + " });\n", + "\n", + " function set_focus () {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "}\n", + "\n", + "mpl.figure.prototype._init_toolbar = function() {\n", + " var fig = this;\n", + "\n", + " var nav_element = $('
');\n", + " nav_element.attr('style', 'width: 100%');\n", + " this.root.append(nav_element);\n", + "\n", + " // Define a callback function for later on.\n", + " function toolbar_event(event) {\n", + " return fig.toolbar_button_onclick(event['data']);\n", + " }\n", + " function toolbar_mouse_event(event) {\n", + " return fig.toolbar_button_onmouseover(event['data']);\n", + " }\n", + "\n", + " for(var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " // put a spacer in here.\n", + " continue;\n", + " }\n", + " var button = $('');\n", + " button.click(method_name, toolbar_event);\n", + " button.mouseover(tooltip, toolbar_mouse_event);\n", + " nav_element.append(button);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = $('');\n", + " nav_element.append(status_bar);\n", + " this.message = status_bar[0];\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = $('
');\n", + " var button = $('');\n", + " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", + " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", + " buttongrp.append(button);\n", + " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", + " titlebar.prepend(buttongrp);\n", + "}\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(el){\n", + " var fig = this\n", + " el.on(\"remove\", function(){\n", + "\tfig.close_ws(fig, {});\n", + " });\n", + "}\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(el){\n", + " // this is important to make the div 'focusable\n", + " el.attr('tabindex', 0)\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " }\n", + " else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._key_event_extra = function(event, name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager)\n", + " manager = IPython.keyboard_manager;\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which == 13) {\n", + " this.canvas_div.blur();\n", + " // select the cell after this one\n", + " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", + " IPython.notebook.select(index + 1);\n", + " }\n", + "}\n", + "\n", + "mpl.figure.prototype.handle_save = function(fig, msg) {\n", + " fig.ondownload(fig, null);\n", + "}\n", + "\n", + "\n", + "mpl.find_output_cell = function(html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] == html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel != null) {\n", + " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(2476822.8, 2835339.2, 1065207.09325, 1300947.6697500001)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "GWR_plot = GWR_large_clusters.sample(200000)\n", + "cmap = categorical_cmap( GWR_plot, 'clusterID', cmap = 'tab10', N = 10 )\n", + "\n", + "plt.figure()\n", + "plt.scatter(GWR_plot[coords[0]], GWR_plot[coords[1]], c = GWR_plot['clusterID'], s = 2, cmap = cmap)\n", + "plt.colorbar()\n", + "plt.axis('equal')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Perform kmeans on the remaining clusters" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5 4748.771876183521\n", + "Fit and predict in 0.07s\n" + ] + } + ], + "source": [ + "for cID, d in large_clusters.iterrows():\n", + " if cID == -1: continue\n", + " print(cID, d.peak_demand_kW)\n", + " \n", + " GWR_sel = GWR[GWR.clusterID == cID].copy()\n", + " N_clust = int(d.peak_demand_kW / 1e3)\n", + " \n", + " model = KMeans(n_clusters = N_clust)\n", + "\n", + " tt = time.time()\n", + " GWR_sel['clusterID'] = model.fit_predict( GWR_sel[coords] )\n", + " print('Fit and predict in %.2fs' %(time.time()-tt))\n", + " \n", + " clusterDemand,_ = get_cluster_demand(GWR_sel, residential_demand, 'clusterID')\n", + " cluster_peaks = clusterDemand.max(dim = ['timestamp', 'daytype']).rename('peak_demand_kW').to_dataframe()\n", + " \n", + " # Add new cluster IDs (to replace old)\n", + " # Remove old cluster peak and append new ones\n", + " \n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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');\n", + " this._root_extra_style(this.root)\n", + " this.root.attr('style', 'display: inline-block');\n", + "\n", + " $(parent_element).append(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", + " fig.send_message(\"send_image_mode\", {});\n", + " if (mpl.ratio != 1) {\n", + " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", + " }\n", + " fig.send_message(\"refresh\", {});\n", + " }\n", + "\n", + " this.imageObj.onload = function() {\n", + " if (fig.image_mode == 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function() {\n", + " fig.ws.close();\n", + " }\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "}\n", + "\n", + "mpl.figure.prototype._init_header = function() {\n", + " var titlebar = $(\n", + " '
');\n", + " var titletext = $(\n", + " '
');\n", + " titlebar.append(titletext)\n", + " this.root.append(titlebar);\n", + " this.header = titletext[0];\n", + "}\n", + "\n", + "\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._init_canvas = function() {\n", + " var fig = this;\n", + "\n", + " var canvas_div = $('
');\n", + "\n", + " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", + "\n", + " function canvas_keyboard_event(event) {\n", + " return fig.key_event(event, event['data']);\n", + " }\n", + "\n", + " canvas_div.keydown('key_press', canvas_keyboard_event);\n", + " canvas_div.keyup('key_release', canvas_keyboard_event);\n", + " this.canvas_div = canvas_div\n", + " this._canvas_extra_style(canvas_div)\n", + " this.root.append(canvas_div);\n", + "\n", + " var canvas = $('');\n", + " canvas.addClass('mpl-canvas');\n", + " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", + "\n", + " this.canvas = canvas[0];\n", + " this.context = canvas[0].getContext(\"2d\");\n", + "\n", + " var backingStore = this.context.backingStorePixelRatio ||\n", + "\tthis.context.webkitBackingStorePixelRatio ||\n", + "\tthis.context.mozBackingStorePixelRatio ||\n", + "\tthis.context.msBackingStorePixelRatio ||\n", + "\tthis.context.oBackingStorePixelRatio ||\n", + "\tthis.context.backingStorePixelRatio || 1;\n", + "\n", + " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband = $('');\n", + " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", + "\n", + " var pass_mouse_events = true;\n", + "\n", + " canvas_div.resizable({\n", + " start: function(event, ui) {\n", + " pass_mouse_events = false;\n", + " },\n", + " resize: function(event, ui) {\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " stop: function(event, ui) {\n", + " pass_mouse_events = true;\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " });\n", + "\n", + " function mouse_event_fn(event) {\n", + " if (pass_mouse_events)\n", + " return fig.mouse_event(event, event['data']);\n", + " }\n", + "\n", + " rubberband.mousedown('button_press', mouse_event_fn);\n", + " rubberband.mouseup('button_release', mouse_event_fn);\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband.mousemove('motion_notify', mouse_event_fn);\n", + "\n", + " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", + " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", + "\n", + " canvas_div.on(\"wheel\", function (event) {\n", + " event = event.originalEvent;\n", + " event['data'] = 'scroll'\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " mouse_event_fn(event);\n", + " });\n", + "\n", + " canvas_div.append(canvas);\n", + " canvas_div.append(rubberband);\n", + "\n", + " this.rubberband = rubberband;\n", + " this.rubberband_canvas = rubberband[0];\n", + " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", + " this.rubberband_context.strokeStyle = \"#000000\";\n", + "\n", + " this._resize_canvas = function(width, height) {\n", + " // Keep the size of the canvas, canvas container, and rubber band\n", + " // canvas in synch.\n", + " canvas_div.css('width', width)\n", + " canvas_div.css('height', height)\n", + "\n", + " canvas.attr('width', width * mpl.ratio);\n", + " canvas.attr('height', height * mpl.ratio);\n", + " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", + "\n", + " rubberband.attr('width', width);\n", + " rubberband.attr('height', height);\n", + " }\n", + "\n", + " // Set the figure to an initial 600x600px, this will subsequently be updated\n", + " // upon first draw.\n", + " this._resize_canvas(600, 600);\n", + "\n", + " // Disable right mouse context menu.\n", + " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", + " return false;\n", + " });\n", + "\n", + " function set_focus () {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "}\n", + "\n", + "mpl.figure.prototype._init_toolbar = function() {\n", + " var fig = this;\n", + "\n", + " var nav_element = $('
');\n", + " nav_element.attr('style', 'width: 100%');\n", + " this.root.append(nav_element);\n", + "\n", + " // Define a callback function for later on.\n", + " function toolbar_event(event) {\n", + " return fig.toolbar_button_onclick(event['data']);\n", + " }\n", + " function toolbar_mouse_event(event) {\n", + " return fig.toolbar_button_onmouseover(event['data']);\n", + " }\n", + "\n", + " for(var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " // put a spacer in here.\n", + " continue;\n", + " }\n", + " var button = $('
    • clusterID
      (clusterID)
      int64
      0 1 2 3
      array([0, 1, 2, 3], dtype=int64)
    • " + ], + "text/plain": [ + "\n", + "array([1769822.84647892, 727945.94246801, 1674470.3887543 ,\n", + " 576532.69848229])\n", + "Coordinates:\n", + " * clusterID (clusterID) int64 0 1 2 3" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clusterDemand.max(dim = ['timestamp', 'daytype'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/clustering/.ipynb_checkpoints/preprocess_new_GWR-checkpoint.ipynb b/clustering/.ipynb_checkpoints/preprocess_new_GWR-checkpoint.ipynb new file mode 100644 index 0000000..7fec515 --- /dev/null +++ b/clustering/.ipynb_checkpoints/preprocess_new_GWR-checkpoint.ipynb @@ -0,0 +1,6 @@ +{ + "cells": [], + "metadata": {}, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/clustering/cluster_GWR.ipynb b/clustering/cluster_GWR.ipynb new file mode 100644 index 0000000..f411dd2 --- /dev/null +++ b/clustering/cluster_GWR.ipynb @@ -0,0 +1,12673 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import xarray as xr\n", + "\n", + "from sklearn.cluster import DBSCAN\n", + "from sklearn.cluster import KMeans\n", + "\n", + "import os\n", + "import time\n", + "import calendar\n", + "\n", + "%matplotlib notebook\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def categorical_cmap(df, column, cmap = 'tab20', N = 20):\n", + " # Get colormap data\n", + " c = matplotlib.cm.get_cmap( cmap )(np.linspace(0., 1, N))\n", + " \n", + " # Get length of series, number of total copies and remainder\n", + " l = len(df[ column ].unique())\n", + " n = int(np.floor(l / N))\n", + " r = l % N\n", + " \n", + " # Concatenate colormap\n", + " clist = [c]*n\n", + " clist.append( c[:r,:] )\n", + " colors = np.vstack(clist)\n", + " \n", + " # return colormap\n", + " return matplotlib.colors.LinearSegmentedColormap.from_list('%s_concat' %cmap, colors)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def get_cluster_demand(BLD_df, demand, grouping_col, uniqueID_col = 'EGID',\n", + " dwell_nb = 'N_DWELL', canton_id = 'GDEKT', \n", + " SFH_demand = 'maison', MFH_demand = 'appart', \n", + " communal_demand = 'communal_rest', communal_demand_GE = 'communal_GVA'):\n", + "\n", + " SFH = BLD_df[BLD_df[dwell_nb] == 1]\n", + " MFH = BLD_df[BLD_df[dwell_nb] > 1]\n", + " \n", + " building_counts = xr.Dataset({ 'N_SFH' : SFH.groupby( grouping_col )[ uniqueID_col ].count(),\n", + " 'N_DWELL' : MFH.groupby( grouping_col )[ dwell_nb ].sum(),\n", + " 'N_MFH' : MFH[MFH.GDEKT != 'GE'].groupby( grouping_col )[ uniqueID_col ].count(),\n", + " 'N_MFH_GE': MFH[MFH.GDEKT == 'GE'].groupby( grouping_col )[ uniqueID_col ].count() }\n", + " ).fillna(0)\n", + "\n", + " cluster_demand = ( building_counts['N_SFH'] * demand[SFH_demand] \n", + " + building_counts['N_DWELL'] * demand[MFH_demand]\n", + " + building_counts['N_MFH'] * demand[communal_demand] \n", + " + building_counts['N_MFH_GE']* demand[communal_demand_GE] )\n", + "\n", + " return [cluster_demand, building_counts]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load GWR" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "GWR_fp = 'D:\\Building_data\\OFS\\Buildings\\GWR_MADD_Export_MADD-20200715-A3_20200722'\n", + "GWR = pd.read_csv(os.path.join(GWR_fp, 'GWR_GEB_w_N_WHG.csv'))" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "coords = ['GKODE', 'GKODN']\n", + "GWR = GWR.dropna(subset = coords) # can only cluster GWR entries with coordinate information" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    " + ], + "text/plain": [ + " EGID GKODE GKODN GSTAT GKAT GKLAS GGDENR GDEKT \\\n", + "0 1600000 2679684.326 1237449.553 1004.0 1020.0 1122.0 1 ZH \n", + "1 1600001 2679702.150 1237493.958 1004.0 1030.0 1110.0 1 ZH \n", + "2 1600002 2679713.052 1237475.814 1004.0 1030.0 1121.0 1 ZH \n", + "3 1600003 2679655.455 1237516.450 1004.0 1020.0 1122.0 1 ZH \n", + "4 1600005 2679749.269 1237529.346 1004.0 1020.0 1110.0 1 ZH \n", + "\n", + " N_DWELL \n", + "0 3.0 \n", + "1 1.0 \n", + "2 2.0 \n", + "3 3.0 \n", + "4 1.0 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "GWR.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Extract only Canton GE for testing\n", + "# GWR = GWR[GWR.GDEKT == 'GE']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load electricity demand" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "demand_fp = 'D:\\Demand' # '/work/hyenergy/output/demand'\n", + "residential_demand = xr.open_dataset(os.path.join(demand_fp, 'electricity_demand_residential.nc')) " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset>\n",
+       "Dimensions:        (daytype: 2, timestamp: 288)\n",
+       "Coordinates:\n",
+       "  * timestamp      (timestamp) datetime64[ns] 2001-01-15 ... 2001-12-15T23:00:00\n",
+       "  * daytype        (daytype) object 'weekday' 'weekend'\n",
+       "Data variables:\n",
+       "    appart         (timestamp, daytype) float64 162.3 181.7 ... 233.7 227.8\n",
+       "    maison         (timestamp, daytype) float64 298.2 344.7 ... 377.5 411.3\n",
+       "    communal_GVA   (timestamp, daytype) float64 241.3 557.0 ... 121.0 302.5\n",
+       "    communal_rest  (timestamp, daytype) float64 79.66 183.9 75.14 ... 39.96 99.9\n",
+       "    weight         (timestamp, daytype) float64 0.0001138 ... 0.0001427\n",
+       "    GVA_annual     float64 2.12e+06\n",
+       "    rest_annual    float64 7e+05
    " + ], + "text/plain": [ + "\n", + "Dimensions: (daytype: 2, timestamp: 288)\n", + "Coordinates:\n", + " * timestamp (timestamp) datetime64[ns] 2001-01-15 ... 2001-12-15T23:00:00\n", + " * daytype (daytype) object 'weekday' 'weekend'\n", + "Data variables:\n", + " appart (timestamp, daytype) float64 ...\n", + " maison (timestamp, daytype) float64 ...\n", + " communal_GVA (timestamp, daytype) float64 ...\n", + " communal_rest (timestamp, daytype) float64 ...\n", + " weight (timestamp, daytype) float64 ...\n", + " GVA_annual float64 ...\n", + " rest_annual float64 ..." + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "residential_demand" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Perform DBSCAN" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "EPS = 250\n", + "MIN_SAMPLES = 10\n", + "\n", + "model = DBSCAN(eps = EPS, min_samples = MIN_SAMPLES, n_jobs = -1)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit and predict in 15.19s\n" + ] + } + ], + "source": [ + "tt = time.time()\n", + "GWR['clusterID'] = model.fit_predict( GWR[coords] )\n", + "print('Fit and predict in %.2fs' %(time.time()-tt))" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support. ' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('
    ');\n", + " this._root_extra_style(this.root)\n", + " this.root.attr('style', 'display: inline-block');\n", + "\n", + " $(parent_element).append(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", + " fig.send_message(\"send_image_mode\", {});\n", + " if (mpl.ratio != 1) {\n", + " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", + " }\n", + " fig.send_message(\"refresh\", {});\n", + " }\n", + "\n", + " this.imageObj.onload = function() {\n", + " if (fig.image_mode == 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function() {\n", + " fig.ws.close();\n", + " }\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "}\n", + "\n", + "mpl.figure.prototype._init_header = function() {\n", + " var titlebar = $(\n", + " '
    ');\n", + " var titletext = $(\n", + " '
    ');\n", + " titlebar.append(titletext)\n", + " this.root.append(titlebar);\n", + " this.header = titletext[0];\n", + "}\n", + "\n", + "\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._init_canvas = function() {\n", + " var fig = this;\n", + "\n", + " var canvas_div = $('
    ');\n", + "\n", + " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", + "\n", + " function canvas_keyboard_event(event) {\n", + " return fig.key_event(event, event['data']);\n", + " }\n", + "\n", + " canvas_div.keydown('key_press', canvas_keyboard_event);\n", + " canvas_div.keyup('key_release', canvas_keyboard_event);\n", + " this.canvas_div = canvas_div\n", + " this._canvas_extra_style(canvas_div)\n", + " this.root.append(canvas_div);\n", + "\n", + " var canvas = $('');\n", + " canvas.addClass('mpl-canvas');\n", + " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", + "\n", + " this.canvas = canvas[0];\n", + " this.context = canvas[0].getContext(\"2d\");\n", + "\n", + " var backingStore = this.context.backingStorePixelRatio ||\n", + "\tthis.context.webkitBackingStorePixelRatio ||\n", + "\tthis.context.mozBackingStorePixelRatio ||\n", + "\tthis.context.msBackingStorePixelRatio ||\n", + "\tthis.context.oBackingStorePixelRatio ||\n", + "\tthis.context.backingStorePixelRatio || 1;\n", + "\n", + " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband = $('');\n", + " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", + "\n", + " var pass_mouse_events = true;\n", + "\n", + " canvas_div.resizable({\n", + " start: function(event, ui) {\n", + " pass_mouse_events = false;\n", + " },\n", + " resize: function(event, ui) {\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " stop: function(event, ui) {\n", + " pass_mouse_events = true;\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " });\n", + "\n", + " function mouse_event_fn(event) {\n", + " if (pass_mouse_events)\n", + " return fig.mouse_event(event, event['data']);\n", + " }\n", + "\n", + " rubberband.mousedown('button_press', mouse_event_fn);\n", + " rubberband.mouseup('button_release', mouse_event_fn);\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband.mousemove('motion_notify', mouse_event_fn);\n", + "\n", + " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", + " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", + "\n", + " canvas_div.on(\"wheel\", function (event) {\n", + " event = event.originalEvent;\n", + " event['data'] = 'scroll'\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " mouse_event_fn(event);\n", + " });\n", + "\n", + " canvas_div.append(canvas);\n", + " canvas_div.append(rubberband);\n", + "\n", + " this.rubberband = rubberband;\n", + " this.rubberband_canvas = rubberband[0];\n", + " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", + " this.rubberband_context.strokeStyle = \"#000000\";\n", + "\n", + " this._resize_canvas = function(width, height) {\n", + " // Keep the size of the canvas, canvas container, and rubber band\n", + " // canvas in synch.\n", + " canvas_div.css('width', width)\n", + " canvas_div.css('height', height)\n", + "\n", + " canvas.attr('width', width * mpl.ratio);\n", + " canvas.attr('height', height * mpl.ratio);\n", + " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", + "\n", + " rubberband.attr('width', width);\n", + " rubberband.attr('height', height);\n", + " }\n", + "\n", + " // Set the figure to an initial 600x600px, this will subsequently be updated\n", + " // upon first draw.\n", + " this._resize_canvas(600, 600);\n", + "\n", + " // Disable right mouse context menu.\n", + " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", + " return false;\n", + " });\n", + "\n", + " function set_focus () {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "}\n", + "\n", + "mpl.figure.prototype._init_toolbar = function() {\n", + " var fig = this;\n", + "\n", + " var nav_element = $('
    ');\n", + " nav_element.attr('style', 'width: 100%');\n", + " this.root.append(nav_element);\n", + "\n", + " // Define a callback function for later on.\n", + " function toolbar_event(event) {\n", + " return fig.toolbar_button_onclick(event['data']);\n", + " }\n", + " function toolbar_mouse_event(event) {\n", + " return fig.toolbar_button_onmouseover(event['data']);\n", + " }\n", + "\n", + " for(var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " // put a spacer in here.\n", + " continue;\n", + " }\n", + " var button = $('');\n", + " button.click(method_name, toolbar_event);\n", + " button.mouseover(tooltip, toolbar_mouse_event);\n", + " nav_element.append(button);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = $('');\n", + " nav_element.append(status_bar);\n", + " this.message = status_bar[0];\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = $('
    ');\n", + " var button = $('');\n", + " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", + " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", + " buttongrp.append(button);\n", + " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", + " titlebar.prepend(buttongrp);\n", + "}\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(el){\n", + " var fig = this\n", + " el.on(\"remove\", function(){\n", + "\tfig.close_ws(fig, {});\n", + " });\n", + "}\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(el){\n", + " // this is important to make the div 'focusable\n", + " el.attr('tabindex', 0)\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " }\n", + " else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._key_event_extra = function(event, name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager)\n", + " manager = IPython.keyboard_manager;\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which == 13) {\n", + " this.canvas_div.blur();\n", + " // select the cell after this one\n", + " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", + " IPython.notebook.select(index + 1);\n", + " }\n", + "}\n", + "\n", + "mpl.figure.prototype.handle_save = function(fig, msg) {\n", + " fig.ondownload(fig, null);\n", + "}\n", + "\n", + "\n", + "mpl.find_output_cell = function(html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] == html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel != null) {\n", + " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(2476823.95, 2835315.05, 1065207.09325, 1300947.6697500001)" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "GWR_plot = GWR_large_clusters.sample(200000)\n", + "cmap = categorical_cmap( GWR_plot, 'clusterID', cmap = 'tab10', N = 10 )\n", + "\n", + "plt.figure()\n", + "plt.scatter(GWR_plot[coords[0]], GWR_plot[coords[1]], c = GWR_plot['clusterID'], s = 2, cmap = cmap)\n", + "plt.colorbar()\n", + "plt.axis('equal')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Perform kmeans on the remaining clusters" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "GWR_dbscan = GWR.copy()\n", + "peak_demand_dbscan = peak_demand.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "GWR = GWR_dbscan.copy()\n", + "peak_demand = peak_demand_dbscan.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5 4748.771876183521\n", + "Fit and predict in 0.08s\n", + "Number of clusters: 5\n", + "Number of clusters with peak demand > 1MW: 1\n", + "7 3142.7866865421934\n", + "Fit and predict in 0.07s\n", + "Number of clusters: 4\n", + "Number of clusters with peak demand > 1MW: 0\n", + "8 1052.6945918788936\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "27 1461.7578438475377\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "28 1973.6873006075984\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "53 19045.901336680265\n", + "Fit and predict in 0.42s\n", + "Number of clusters: 20\n", + "Number of clusters with peak 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1\n", + "2393 24209.096986850393\n", + "Fit and predict in 0.66s\n", + "Number of clusters: 25\n", + "Number of clusters with peak demand > 1MW: 12\n", + "2405 3401.4464144349517\n", + "Fit and predict in 0.08s\n", + "Number of clusters: 4\n", + "Number of clusters with peak demand > 1MW: 2\n", + "2444 1808.9082543779753\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "2445 1108.9194946965788\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "2455 12097.996864956964\n", + "Fit and predict in 0.21s\n", + "Number of clusters: 13\n", + "Number of clusters with peak demand > 1MW: 6\n", + "2461 7212.890790555987\n", + "Fit and predict in 0.13s\n", + "Number of clusters: 8\n", + "Number of clusters with peak demand > 1MW: 5\n", + "2464 2140.199527499092\n", + "Fit and predict in 0.05s\n", + "Number of clusters: 3\n", + "Number of clusters with peak 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"3239 1878.5448527145575\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "3241 8564.046972151022\n", + "Fit and predict in 0.15s\n", + "Number of clusters: 9\n", + "Number of clusters with peak demand > 1MW: 4\n", + "3243 1378.923979145413\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "3245 6055.659859731086\n", + "Fit and predict in 0.09s\n", + "Number of clusters: 7\n", + "Number of clusters with peak demand > 1MW: 2\n", + "3248 2483.467821163019\n", + "Fit and predict in 0.05s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 0\n", + "3249 3816.8265531847874\n", + "Fit and predict in 0.09s\n", + "Number of clusters: 4\n", + "Number of clusters with peak demand > 1MW: 2\n", + "3253 1755.4509257708933\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 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2\n", + "5445 1030.3969241192083\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5447 1493.8688898157122\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5448 2265.975440681409\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5453 1378.4700533235698\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5454 1700.4193911273321\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5458 1307.9722062493354\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5459 1019.5348406953267\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand 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"Number of clusters with peak demand > 1MW: 0\n", + "5690 1037.3321243516098\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5694 1325.9951087964275\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5697 2065.3077644691657\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5698 1173.9166289151442\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5701 1350.2263899686382\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5702 1018.4528456972097\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5706 1161.6927299286265\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5709 1020.5291537880779\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5711 1048.0384742911158\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5717 1107.6865985804743\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5718 1044.8386347158048\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5730 1008.4443325533888\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5731 1093.9240763306152\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + 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"Number of clusters with peak demand > 1MW: 0\n", + "5867 1396.6643916693477\n", + "Fit and predict in 0.05s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5868 1800.6253203747078\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5870 1063.498947108048\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5871 1616.4063355607143\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "5875 1035.949929896469\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5877 1261.501971568004\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5878 1251.8721204963567\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5882 1178.8953722359647\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5883 1461.1001821876969\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5884 1476.1442680959417\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5885 1009.2862375182689\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5886 1547.2934464933319\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "5890 1568.9691807860677\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5891 1525.914047363833\n", + "Fit and predict in 0.03s\n", + "Number of 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0\n", + "5950 1816.8769128316032\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "5951 1987.8292284793538\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "5953 1755.18922626006\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "5958 1066.7632875565473\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5960 1438.2840978617714\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5961 2389.4558022479114\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 0\n", + "5964 1514.3790194516248\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 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"Number of clusters with peak demand > 1MW: 0\n", + "6217 1764.1391389940318\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6222 1596.8883397910042\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6223 1204.6776497020994\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6226 1624.707162418773\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "6230 2180.238425419132\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6231 1875.4350915148268\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "6232 1050.0785617057763\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6239 1205.1203139625763\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6243 1476.419671601467\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6244 1318.087512346827\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6248 1368.4246996912022\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "6250 1003.3087741744939\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6253 1872.1421099894187\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "6258 1004.4418165191632\n", + "Fit and predict in 0.02s\n", + "Number of 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"Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6339 2256.21417030915\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit and predict in 0.04s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6340 1077.950912952591\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6341 1254.4902504573147\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6345 1120.523620023112\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6346 2800.053026858438\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 1\n", + "6347 1394.4616839924033\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number 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"Number of clusters with peak demand > 1MW: 0\n", + "6369 1150.9634187030424\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6371 1160.3178347092107\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6379 1284.7935129282425\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6380 1242.3691761484656\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6382 1445.8953036545824\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6385 1066.5466946574459\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6392 1199.949543232473\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6393 1423.2954386091094\n", + "Fit and predict in 0.06s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6394 1398.2678180914695\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "6395 1318.9190248571538\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6397 1083.2907869690725\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6398 1059.5352025836487\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6404 1212.016836778224\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6410 1520.6090506678731\n", + "Fit and predict in 0.02s\n", + "Number of 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"Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6495 1303.385339852495\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6501 1224.7597081854142\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6506 1922.1386429641427\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6509 1007.4208982804304\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6511 1232.155728392717\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6514 1023.5498883979049\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6515 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"6527 1306.530419636878\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "6529 1328.9821234221245\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6530 1300.6465622673459\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6533 1094.2890696556265\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6534 1206.3457319476045\n", + "Fit and predict in 0.03s\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6539 1160.4303218568684\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6543 1010.8234678711743\n", + "Fit and predict in 0.03s\n", + 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"Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6608 1399.1351002322265\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6620 1049.0055741177807\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6624 1229.9245632994362\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6627 1008.1885379430557\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6630 1027.0578997206285\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6634 2925.013549581768\n", + "Fit and predict in 0.05s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 1\n", + "6635 2570.9003038042324\n", + "Fit and predict in 0.06s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 1\n", + "6636 2579.5874472468995\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 1\n", + "6639 1376.0529790263893\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6640 1828.440449139687\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "6643 1293.558993333032\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6646 2408.5619146462022\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 1\n", + "6650 1740.5906636818645\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "6654 1609.8306325230656\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6658 1187.8254030863204\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6660 2160.669369073294\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6661 2432.1750342281734\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6662 1148.6345528684915\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6666 1063.040758590615\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6669 1461.389485740063\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6676 2371.538912528385\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6678 1048.1795763192986\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6679 1003.9851034516593\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6680 1059.1910730196614\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6683 2868.4353072006725\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 2\n", + "6686 1853.7655926277055\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "6688 1156.040251277793\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 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clusters with peak demand > 1MW: 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6754 1283.1993567758752\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6755 1578.9129209062742\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6758 1136.3517964744417\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6760 1367.7482509510191\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6764 1507.6139669161266\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6767 1401.5384511151317\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6770 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"6788 1381.4258052924738\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "6792 1099.6020363791886\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6793 1222.3547646523486\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6794 1119.5812400496693\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6796 1249.1226968878145\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6797 1161.34585707051\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6800 1374.230048231267\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 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"Number of clusters with peak demand > 1MW: 0\n", + "6903 1811.1387162814272\n", + "Fit and predict in 0.05s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6904 1146.1020165786383\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6905 1340.8085853964963\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6912 1364.1837262866502\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6913 1897.506646224391\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "6915 1178.139545810708\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6917 1666.8174698426003\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6918 1243.797536867019\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6919 1733.5495993820823\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6921 1240.4483271287604\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6922 1122.5295462854488\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6924 1485.323407943235\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "6927 1121.4313071746033\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6929 1173.977065994469\n", + "Fit and predict in 0.03s\n", + "Number of 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"6948 2039.249786228341\n", + "Fit and predict in 0.05s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6949 1575.165417931489\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6950 1441.0708670849606\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "6953 1091.0272938571115\n", + "Fit and predict in 0.05s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6955 1120.5851826276669\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6956 1015.1138328522095\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6957 1204.0470288542092\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6961 1292.7672530427324\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6964 1345.8199189018508\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6965 1029.2100649259141\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6971 1048.1344553044098\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6972 1957.629637032891\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "6974 1143.1833394987436\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "6975 1075.7205279854143\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand 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"Number of clusters with peak demand > 1MW: 0\n", + "7053 1483.4161313148336\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7060 1084.0156043063764\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7065 1120.9609030907754\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7066 1081.2137195964729\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7067 1419.874359200813\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7070 1230.0833302231626\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7072 1765.4277345029366\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "7075 1224.3987879450597\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7083 1087.8596371208814\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7084 1277.5274402591967\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7086 1279.0875687782716\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7088 1083.2020110984313\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7091 1759.5189022865538\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "7105 1092.631387730679\n", + "Fit and predict in 0.03s\n", + "Number of 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"Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7130 1710.0843629595504\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "7131 1432.3079310725736\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7132 1172.7615733390637\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7136 1019.206532908667\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7137 1426.7543857596809\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7139 1641.2624878817167\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "7143 1182.164433453839\n", + "Fit and predict in 0.02s\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7145 1253.7769927885395\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7146 1323.646771273291\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7148 1578.8857521785108\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7151 1170.6036282995137\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7153 1025.8293079888592\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7157 1321.771243008199\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with 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"Number of clusters with peak demand > 1MW: 0\n", + "7200 1246.087556518255\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7201 1200.9874300662725\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7203 1211.1799106771314\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7205 1206.731606190437\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7209 1136.7840511235681\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7212 1131.8273094805788\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7213 1164.011775242209\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7218 1752.7601282702049\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "7219 1049.0177465706415\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7221 1004.0095618700369\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7222 1782.3625883912257\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "7226 1390.4298433506713\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7232 1439.6300817694953\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7235 1077.1628003043882\n", + "Fit and predict in 0.03s\n", + "Number of 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"Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7373 1106.1353402649145\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7380 1955.792056199989\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "7382 1659.0589714055207\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7384 1257.9564939940337\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7385 1658.9208095616143\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7386 1386.5182460884616\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + 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"7569 1047.8436633237466\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7572 1067.164877092058\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7574 1371.6820293732028\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7575 1969.4054271950024\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "7577 1777.2924414298707\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "7578 1383.374593037256\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7583 1562.3729098317713\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 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0.04s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7604 1489.1991012179858\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "7609 1854.085567483696\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7610 1723.9212553203186\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "7611 1921.420353776046\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "7612 1293.858432670327\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7614 2137.414605081694\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 0\n", + "7624 1862.701085785579\n", + "Fit and predict 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"Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "8380 1161.9065254532763\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "8381 1020.6523911809923\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "8384 1169.1293957371358\n", + "Fit and predict in 0.01s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "8390 1548.7483876872648\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "8395 1072.9489353572683\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "8461 1317.5942193876233\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "8463 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"8587 1033.8905496885745\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "8612 1015.6325985051509\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "8616 1121.0961326164102\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "8639 1184.1939732125443\n", + "Fit and predict in 0.01s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "8653 1340.967978082923\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "8662 1072.8949973811893\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "8665 1005.8756094100685\n", + "Fit and predict in 0.02s\n", + 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"Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9647 1816.7271482056344\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "9651 1784.7419865686447\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9654 1489.469948195124\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9655 1240.2925612464323\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9658 1298.2038355430782\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9663 1209.5066396993238\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9671 1357.3309400282506\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "9673 1011.3744112508328\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9674 1080.2397307291133\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9677 1135.840048364799\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9681 1027.4862723208205\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9683 1162.751024365309\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9687 1035.8178121712485\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9692 1028.3002802493688\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9694 1064.2229857440364\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9710 1159.8084038707213\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9736 1059.3185285698432\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9737 1002.7274823699689\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9750 1108.0905113374997\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9756 1167.4758370383854\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9763 1191.644900546173\n", + "Fit and predict in 0.01s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9768 1143.581756125511\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9770 1284.5331910866907\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9776 1051.9200437559898\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9786 1008.2719563148337\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9787 1271.3764683632774\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9790 1305.8695281383937\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9792 1111.2350084727161\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9795 1307.8783343146752\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9796 1089.208428280641\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9799 1055.7715747079458\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9801 1070.7486413073361\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9807 1207.2806208116026\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9816 1339.4648139512713\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak 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peak demand > 1MW: 0\n", + "9870 1043.3514454045087\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9871 1124.0974388707218\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9873 1118.174402255461\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9876 1064.7809785763257\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9879 1139.8081536831426\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9880 1104.9873529341112\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9882 1369.0986729646936\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9883 1327.744252106613\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9884 1025.2685377451871\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9892 1004.4621212352763\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9893 1027.0067386503597\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9895 1498.9462892264019\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "9898 1662.9685974141646\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9904 1196.9768104706955\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9907 1450.3217715656253\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9922 1000.523451080871\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9925 1103.690473829751\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9931 1178.1806535288936\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9938 1027.8622959539077\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9940 1125.7248684722956\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9942 1083.8008520230574\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9943 1027.045759348013\n", + "Fit and predict in 0.02s\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9963 1135.845636039307\n", + "Fit and predict in 0.01s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9964 1103.8353192669967\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9969 1136.7658491430434\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9972 1374.8637108963267\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9977 1114.065511010893\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "9978 1183.243729386918\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "10000 1019.3874516951997\n", + "Fit and predict in 0.05s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "10005 1296.6237713112716\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "10008 1133.2388693939738\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "10014 1244.3803649785955\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "10018 1250.516905744837\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "10022 1276.6989403438479\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "10028 1437.6642179683442\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "10029 1023.1281504315697\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "10031 1060.926639436165\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "10033 1507.469015236962\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "10036 1246.0307169458613\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "10061 1161.187222827817\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "10069 1441.0910239685802\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "10075 1124.6546145868815\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "10078 1145.218016172216\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "10090 1245.3948357929642\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "10092 1063.7225014585458\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "10103 1109.1901619403677\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "10106 1065.868697218634\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "10107 1333.8881924148552\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "10108 1191.294843225733\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "10115 1005.5757345077976\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "10155 1063.9136301167048\n", + "Fit and predict in 0.01s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "\n", + "Number of remaining large clusters: 4\n", + "\n", + "10362 1010.1898113211469\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "10420 1191.9406384058802\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "10432 1154.4930522305244\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "10523 1083.3315145292227\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "\n", + "Number of remaining large clusters: 0\n", + "\n" + ] + } + ], + "source": [ + "large_clusters = peak_demand[(peak_demand.peak_demand_kW > 1000) & (peak_demand.index >= 0)]\n", + "\n", + "while(len(large_clusters) > 0):\n", + " \n", + " for cID, d in large_clusters.iterrows():\n", + " if cID == -1: continue\n", + " print(cID, d.peak_demand_kW)\n", + "\n", + " GWR_sel = GWR[GWR.clusterID == cID].copy()\n", + " N_clust = int(np.ceil(d.peak_demand_kW / 1e3))\n", + "\n", + " if len(GWR_sel) == 0:\n", + " continue\n", + "\n", + " model = KMeans(n_clusters = N_clust)\n", + "\n", + " tt = time.time()\n", + " GWR_sel['clusterID'] = model.fit_predict( GWR_sel[coords] )\n", + " print('Fit and predict in %.2fs' %(time.time()-tt))\n", + "\n", + " clusterDemand,_ = get_cluster_demand(GWR_sel, residential_demand, 'clusterID')\n", + " cluster_peaks = clusterDemand.max(dim = ['timestamp', 'daytype']).rename('peak_demand_kW').to_dataframe() / 1e3\n", + "\n", + " # Add new cluster IDs (to replace old)\n", + " clusterIdMax = GWR.clusterID.max() + 1\n", + " GWR.loc[GWR.clusterID == cID, 'clusterID'] = clusterIdMax + GWR_sel['clusterID']\n", + "\n", + " # Remove old cluster peak and append new ones\n", + " cluster_peaks.index = cluster_peaks.index + clusterIdMax\n", + "\n", + " peak_demand = peak_demand[peak_demand.index != cID]\n", + " peak_demand = pd.concat([peak_demand, cluster_peaks])\n", + "\n", + " print('Number of clusters: %d' %(len(cluster_peaks)))\n", + " print('Number of clusters with peak demand > 1MW: %d' %(len(cluster_peaks[cluster_peaks.peak_demand_kW > 1000])))\n", + " \n", + " large_clusters = peak_demand[(peak_demand.peak_demand_kW > 1000) & (peak_demand.index >= 0)]\n", + " print('\\nNumber of remaining large clusters: %d\\n' %(len(large_clusters)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluate the results" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support. ' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('
    ');\n", + " this._root_extra_style(this.root)\n", + " this.root.attr('style', 'display: inline-block');\n", + "\n", + " $(parent_element).append(this.root);\n", + "\n", + " this._init_header(this);\n", + " this._init_canvas(this);\n", + " this._init_toolbar(this);\n", + "\n", + " var fig = this;\n", + "\n", + " this.waiting = false;\n", + "\n", + " this.ws.onopen = function () {\n", + " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n", + " fig.send_message(\"send_image_mode\", {});\n", + " if (mpl.ratio != 1) {\n", + " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n", + " }\n", + " fig.send_message(\"refresh\", {});\n", + " }\n", + "\n", + " this.imageObj.onload = function() {\n", + " if (fig.image_mode == 'full') {\n", + " // Full images could contain transparency (where diff images\n", + " // almost always do), so we need to clear the canvas so that\n", + " // there is no ghosting.\n", + " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n", + " }\n", + " fig.context.drawImage(fig.imageObj, 0, 0);\n", + " };\n", + "\n", + " this.imageObj.onunload = function() {\n", + " fig.ws.close();\n", + " }\n", + "\n", + " this.ws.onmessage = this._make_on_message_function(this);\n", + "\n", + " this.ondownload = ondownload;\n", + "}\n", + "\n", + "mpl.figure.prototype._init_header = function() {\n", + " var titlebar = $(\n", + " '
    ');\n", + " var titletext = $(\n", + " '
    ');\n", + " titlebar.append(titletext)\n", + " this.root.append(titlebar);\n", + " this.header = titletext[0];\n", + "}\n", + "\n", + "\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._init_canvas = function() {\n", + " var fig = this;\n", + "\n", + " var canvas_div = $('
    ');\n", + "\n", + " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", + "\n", + " function canvas_keyboard_event(event) {\n", + " return fig.key_event(event, event['data']);\n", + " }\n", + "\n", + " canvas_div.keydown('key_press', canvas_keyboard_event);\n", + " canvas_div.keyup('key_release', canvas_keyboard_event);\n", + " this.canvas_div = canvas_div\n", + " this._canvas_extra_style(canvas_div)\n", + " this.root.append(canvas_div);\n", + "\n", + " var canvas = $('');\n", + " canvas.addClass('mpl-canvas');\n", + " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", + "\n", + " this.canvas = canvas[0];\n", + " this.context = canvas[0].getContext(\"2d\");\n", + "\n", + " var backingStore = this.context.backingStorePixelRatio ||\n", + "\tthis.context.webkitBackingStorePixelRatio ||\n", + "\tthis.context.mozBackingStorePixelRatio ||\n", + "\tthis.context.msBackingStorePixelRatio ||\n", + "\tthis.context.oBackingStorePixelRatio ||\n", + "\tthis.context.backingStorePixelRatio || 1;\n", + "\n", + " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband = $('');\n", + " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", + "\n", + " var pass_mouse_events = true;\n", + "\n", + " canvas_div.resizable({\n", + " start: function(event, ui) {\n", + " pass_mouse_events = false;\n", + " },\n", + " resize: function(event, ui) {\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " stop: function(event, ui) {\n", + " pass_mouse_events = true;\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " });\n", + "\n", + " function mouse_event_fn(event) {\n", + " if (pass_mouse_events)\n", + " return fig.mouse_event(event, event['data']);\n", + " }\n", + "\n", + " rubberband.mousedown('button_press', mouse_event_fn);\n", + " rubberband.mouseup('button_release', mouse_event_fn);\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband.mousemove('motion_notify', mouse_event_fn);\n", + "\n", + " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", + " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", + "\n", + " canvas_div.on(\"wheel\", function (event) {\n", + " event = event.originalEvent;\n", + " event['data'] = 'scroll'\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " mouse_event_fn(event);\n", + " });\n", + "\n", + " canvas_div.append(canvas);\n", + " canvas_div.append(rubberband);\n", + "\n", + " this.rubberband = rubberband;\n", + " this.rubberband_canvas = rubberband[0];\n", + " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", + " this.rubberband_context.strokeStyle = \"#000000\";\n", + "\n", + " this._resize_canvas = function(width, height) {\n", + " // Keep the size of the canvas, canvas container, and rubber band\n", + " // canvas in synch.\n", + " canvas_div.css('width', width)\n", + " canvas_div.css('height', height)\n", + "\n", + " canvas.attr('width', width * mpl.ratio);\n", + " canvas.attr('height', height * mpl.ratio);\n", + " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", + "\n", + " rubberband.attr('width', width);\n", + " rubberband.attr('height', height);\n", + " }\n", + "\n", + " // Set the figure to an initial 600x600px, this will subsequently be updated\n", + " // upon first draw.\n", + " this._resize_canvas(600, 600);\n", + "\n", + " // Disable right mouse context menu.\n", + " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", + " return false;\n", + " });\n", + "\n", + " function set_focus () {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "}\n", + "\n", + "mpl.figure.prototype._init_toolbar = function() {\n", + " var fig = this;\n", + "\n", + " var nav_element = $('
    ');\n", + " nav_element.attr('style', 'width: 100%');\n", + " this.root.append(nav_element);\n", + "\n", + " // Define a callback function for later on.\n", + " function toolbar_event(event) {\n", + " return fig.toolbar_button_onclick(event['data']);\n", + " }\n", + " function toolbar_mouse_event(event) {\n", + " return fig.toolbar_button_onmouseover(event['data']);\n", + " }\n", + "\n", + " for(var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " // put a spacer in here.\n", + " continue;\n", + " }\n", + " var button = $('
    " + ], + "text/plain": [ + "\n", + "Dimensions: (clusterID: 8994, daytype: 2, timestamp: 288)\n", + "Coordinates:\n", + " * clusterID (clusterID) int64 -1 0 1 2 3 ... 10607 10608 10609 10610\n", + " * timestamp (timestamp) datetime64[ns] 2001-01-15 ... 2001-12-15T23:0...\n", + " * daytype (daytype) object 'weekday' 'weekend'\n", + "Data variables:\n", + " cluster_demand (clusterID, timestamp, daytype) float64 2.833e+07 ... 2.1...\n", + " N_SFH (clusterID) float64 6.067e+04 31.0 24.0 21.0 ... 6.0 4.0 1.0\n", + " N_DWELL (clusterID) float64 5.226e+04 99.0 30.0 ... 854.0 881.0\n", + " N_MFH (clusterID) float64 2.167e+04 20.0 11.0 ... 35.0 0.0 0.0\n", + " N_MFH_GE (clusterID) float64 124.0 0.0 0.0 0.0 ... 0.0 0.0 61.0 40.0\n", + " appart (timestamp, daytype) float64 162.3 181.7 ... 233.7 227.8\n", + " maison (timestamp, daytype) float64 298.2 344.7 ... 377.5 411.3\n", + " communal_GVA (timestamp, daytype) float64 241.3 557.0 ... 121.0 302.5\n", + " communal_rest (timestamp, daytype) float64 79.66 183.9 ... 39.96 99.9" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "electricity_demand_out" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Save results" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "electricity_demand_out.to_netcdf('demand_clusters.nc')\n", + "GWR.to_csv('GWR_clusters.csv', index = False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Single iteration" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5 4748.771876183521\n", + "Fit and predict in 0.08s\n", + "Number of clusters: 5\n", + "Number of clusters with peak demand > 1MW: 2\n", + "7 3142.7866865421934\n", + "Fit and predict in 0.07s\n", + "Number of clusters: 4\n", + "Number of clusters with peak demand > 1MW: 0\n", + "8 1052.6945918788936\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "27 1461.7578438475377\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "28 1973.6873006075984\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "53 19045.901336680265\n", + "Fit and predict in 0.40s\n", + "Number of clusters: 20\n", + "Number of clusters with peak demand > 1MW: 11\n", + "54 1260.2153033928041\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "74 8771.04338957972\n", + "Fit and predict in 0.14s\n", + "Number of clusters: 9\n", + "Number of clusters with peak demand > 1MW: 4\n", + "75 274809.9639263198\n", + "Fit and predict in 43.23s\n", + "Number of clusters: 275\n", + "Number of clusters with peak demand > 1MW: 109\n", + "76 3371.492828386601\n", + "Fit and predict in 0.07s\n", + "Number of clusters: 4\n", + "Number of clusters with peak demand > 1MW: 2\n", + "81 1673.8457497431873\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "83 4560.498050015758\n", + "Fit and predict in 0.07s\n", + "Number of clusters: 5\n", + "Number of clusters with peak demand > 1MW: 2\n", + "86 1381.6941802879114\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "100 1529.7096532265289\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "108 2009.8140279070558\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 0\n", + "109 5489.541329984773\n", + "Fit and predict in 0.08s\n", + "Number of clusters: 6\n", + "Number of clusters with peak demand > 1MW: 2\n", + "110 1254.2167024615835\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "113 3134.449203203304\n", + "Fit and predict in 0.06s\n", + "Number of clusters: 4\n", + "Number of clusters with peak demand > 1MW: 2\n", + "118 3604.771704347395\n", + "Fit and predict in 0.09s\n", + "Number of clusters: 4\n", + "Number of clusters with peak demand > 1MW: 2\n", + "119 2935.382938427621\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 1\n", + "122 1550.4228698305792\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "123 1376.9202783239434\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "128 2418.0720920091253\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 0\n", + "143 1422.8774877606388\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "146 1711.33518766883\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "151 8780.44462405959\n", + "Fit and predict in 0.19s\n", + "Number of clusters: 9\n", + "Number of clusters with peak demand > 1MW: 4\n", + "154 6605.561533793985\n", + "Fit and predict in 0.11s\n", + "Number of clusters: 7\n", + "Number of clusters with peak demand > 1MW: 4\n", + "164 1701.8838848702367\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "166 9332.012200543095\n", + "Fit and predict in 0.17s\n", + "Number of clusters: 10\n", + "Number of clusters with peak demand > 1MW: 4\n", + "167 1147.3422412276361\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "171 2143.1147364126573\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 0\n", + "174 2936.3451563331487\n", + "Fit and predict in 0.05s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 2\n", + "184 3034.1824784991095\n", + "Fit and predict in 0.07s\n", + "Number of clusters: 4\n", + "Number of clusters with peak demand > 1MW: 1\n", + "198 13376.316738818705\n", + "Fit and predict in 0.25s\n", + "Number of clusters: 14\n", + "Number of clusters with peak demand > 1MW: 6\n", + "215 1935.8330079883397\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "217 1010.5669010007416\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "225 3697.379288550638\n", + "Fit and predict in 0.07s\n", + "Number of clusters: 4\n", + 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"Number of clusters with peak demand > 1MW: 2\n", + "269 9668.78027048671\n", + "Fit and predict in 0.16s\n", + "Number of clusters: 10\n", + "Number of clusters with peak demand > 1MW: 5\n", + "288 1307.3535992165357\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "295 39159.02645775045\n", + "Fit and predict in 1.18s\n", + "Number of clusters: 40\n", + "Number of clusters with peak demand > 1MW: 18\n", + "301 1015.9634988141743\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "314 2154.0456507036556\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 0\n", + "322 2168.8032244836245\n", + "Fit and predict in 0.05s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 1\n", + "335 1665.6397746856176\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "339 3719.221690233578\n", + "Fit and predict in 0.07s\n", + "Number of clusters: 4\n", + "Number of clusters with peak demand > 1MW: 1\n", + "362 1497.2308842043726\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "363 2879.7914687262896\n", + "Fit and predict in 0.05s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 1\n", + "379 1440.8270854517207\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "407 1243.583834010653\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "412 1941.8124254605532\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "416 1045.769115224811\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "427 4509.955382920604\n", + "Fit and predict in 0.11s\n", + "Number of clusters: 5\n", + "Number of clusters with peak demand > 1MW: 2\n", + "431 1036.880588462829\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "456 1066.0238387630502\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "469 1897.9216295070605\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "481 6676.140237690797\n", + "Fit and predict in 0.14s\n", + "Number of clusters: 7\n", + "Number of clusters with peak demand > 1MW: 2\n", + "501 4183.922351883715\n", + "Fit and predict in 0.09s\n", + "Number of clusters: 5\n", + "Number of clusters with peak demand > 1MW: 1\n", + "513 87353.38407767846\n", + "Fit and predict in 5.05s\n", + "Number of clusters: 88\n", + "Number of clusters with peak demand > 1MW: 31\n", + "525 1715.2732518937626\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "527 1276.6111127790807\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "544 1337.0791927598148\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "562 2032.1407120382937\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 1\n", + "566 33782.537983539296\n", + "Fit and predict in 0.90s\n", + "Number of clusters: 34\n", + "Number of clusters with peak demand > 1MW: 13\n", + "571 1337.6027972203601\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "575 10583.264445084806\n", + "Fit and predict in 0.21s\n", + "Number of clusters: 11\n", + "Number of clusters with peak demand > 1MW: 5\n", + "580 1636.4224115309028\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "585 3909.285642282258\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit and predict in 0.06s\n", + "Number of clusters: 4\n", + "Number of clusters with peak demand > 1MW: 1\n", + "588 7116.906660544949\n", + "Fit and predict in 0.13s\n", + "Number of clusters: 8\n", + "Number of clusters with peak demand > 1MW: 2\n", + "592 1545.163295357455\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "643 2261.0657950261198\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 1\n", + "650 1714.6335103743013\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "662 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"Fit and predict in 0.16s\n", + "Number of clusters: 12\n", + "Number of clusters with peak demand > 1MW: 6\n", + "1606 1256.5034305790557\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "1618 3720.604435777931\n", + "Fit and predict in 0.07s\n", + "Number of clusters: 4\n", + "Number of clusters with peak demand > 1MW: 2\n", + "1619 1208.9282109031526\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "1623 2877.6702329190407\n", + "Fit and predict in 0.06s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 1\n", + "1644 4783.482242523502\n", + "Fit and predict in 0.11s\n", + "Number of clusters: 5\n", + "Number of clusters with peak demand > 1MW: 3\n", + "1652 1065.550746638998\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "1659 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"Number of clusters with peak demand > 1MW: 0\n", + "2210 1165.7339751971424\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "2222 1249.1556045990947\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "2231 3830.7125427024507\n", + "Fit and predict in 0.05s\n", + "Number of clusters: 4\n", + "Number of clusters with peak demand > 1MW: 1\n", + "2241 1188.723848678434\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "2244 2783.725060020654\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 1\n", + "2245 6324.884192268525\n", + "Fit and predict in 0.13s\n", + "Number of clusters: 7\n", + "Number of clusters with peak demand > 1MW: 2\n", + "2249 1292.9313242229978\n", + "Fit and predict in 0.06s\n", + "Number of clusters: 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"stdout", + "output_type": "stream", + "text": [ + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "4123 1000.6176216367428\n", + "Fit and predict in 0.02s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 0\n", + "4179 3811.8625731747557\n", + "Fit and predict in 0.09s\n", + "Number of clusters: 4\n", + "Number of clusters with peak demand > 1MW: 2\n", + "4189 3833.3375730064763\n", + "Fit and predict in 0.06s\n", + "Number of clusters: 4\n", + "Number of clusters with peak demand > 1MW: 2\n", + "4191 2622.7803497260143\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 3\n", + "Number of clusters with peak demand > 1MW: 1\n", + "4192 1900.1463287330453\n", + "Fit and predict in 0.04s\n", + "Number of clusters: 2\n", + "Number of clusters with peak demand > 1MW: 1\n", + "4206 1803.768201165814\n", + "Fit and predict in 0.03s\n", + "Number of clusters: 2\n", + "Number of clusters with 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clusterDemand.max(dim = ['timestamp', 'daytype']).rename('peak_demand_kW').to_dataframe() / 1e3\n", + " \n", + " # Add new cluster IDs (to replace old)\n", + " clusterIdMax = GWR.clusterID.max() + 1\n", + " GWR.loc[GWR.clusterID == cID, 'clusterID'] = clusterIdMax + GWR_sel['clusterID']\n", + " \n", + " # Remove old cluster peak and append new ones\n", + " cluster_peaks.index = cluster_peaks.index + clusterIdMax\n", + " \n", + " peak_demand = peak_demand[peak_demand.index != cID]\n", + " peak_demand = pd.concat([peak_demand, cluster_peaks])\n", + "\n", + " print('Number of clusters: %d' %(len(cluster_peaks)))\n", + " print('Number of clusters with peak demand > 1MW: %d' %(len(cluster_peaks[cluster_peaks.peak_demand_kW > 1000])))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/clustering/preprocess_new_GWR.ipynb b/clustering/preprocess_new_GWR.ipynb new file mode 100644 index 0000000..7cf0af3 --- /dev/null +++ b/clustering/preprocess_new_GWR.ipynb @@ -0,0 +1,6818 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "import geopandas as gpd\n", + "import xarray as xr\n", + "\n", + "import os\n", + "import calendar\n", + "\n", + "%matplotlib notebook\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def get_yearly_sum( ds, var = None ):\n", + "# create an xarray datasets that contains, for each timestamp in \"ds\", the number of repetitions of that hour in the month\n", + "# and sums all values over a year \n", + "\n", + " days_of_each_month = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]\n", + " month = range(1,13)\n", + " \n", + " days_of_month = pd.DataFrame( data = [ month, days_of_each_month ], index = ['month', 'n_days'] ).transpose()\n", + " \n", + " timestamps = ds.timestamp.to_dataframe()\n", + " timestamps['month'] = timestamps.timestamp.dt.month\n", + " \n", + " days_of_month = timestamps.merge( days_of_month, on = 'month', how = 'left' ).drop( columns = 'month' ).set_index('timestamp').to_xarray()\n", + " \n", + " if var is not None:\n", + " annual_sum = (ds[var] * days_of_month.n_days).sum( dim = 'timestamp' )\n", + " else:\n", + " annual_sum = (ds * days_of_month.n_days).sum( dim = 'timestamp' )\n", + " \n", + " week_weighted = annual_sum.sel( daytype = 'weekday' ) * 5/7 + annual_sum.sel( daytype = 'weekend' ) * 2/7\n", + " return week_weighted" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Load GWR" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "GWR_fp = 'D:\\Building_data\\OFS\\Buildings\\GWR_MADD_Export_MADD-20200715-A3_20200722'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Building information" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "BLD_fp = 'GWR_MADD_GEB-10_Data_MADD-20200715-A3_20200722.dsv'\n", + "BLD_cols = ['EGID', 'GKODE', 'GKODN', 'GSTAT', 'GKAT', 'GKLAS', 'GGDENR', 'GDEKT']\n", + "BLD = pd.read_csv(os.path.join(GWR_fp, BLD_fp), sep = '\\t', usecols = BLD_cols)\n", + "\n", + "# Extract only those dwellings that are currently active (\"Wohnung bestehend\")\n", + "BLD = BLD[BLD.GSTAT == 1004]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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    ');\n", + " var titletext = $(\n", + " '
    ');\n", + " titlebar.append(titletext)\n", + " this.root.append(titlebar);\n", + " this.header = titletext[0];\n", + "}\n", + "\n", + "\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._init_canvas = function() {\n", + " var fig = this;\n", + "\n", + " var canvas_div = $('
    ');\n", + "\n", + " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n", + "\n", + " function canvas_keyboard_event(event) {\n", + " return fig.key_event(event, event['data']);\n", + " }\n", + "\n", + " canvas_div.keydown('key_press', canvas_keyboard_event);\n", + " canvas_div.keyup('key_release', canvas_keyboard_event);\n", + " this.canvas_div = canvas_div\n", + " this._canvas_extra_style(canvas_div)\n", + " this.root.append(canvas_div);\n", + "\n", + " var canvas = $('');\n", + " canvas.addClass('mpl-canvas');\n", + " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n", + "\n", + " this.canvas = canvas[0];\n", + " this.context = canvas[0].getContext(\"2d\");\n", + "\n", + " var backingStore = this.context.backingStorePixelRatio ||\n", + "\tthis.context.webkitBackingStorePixelRatio ||\n", + "\tthis.context.mozBackingStorePixelRatio ||\n", + "\tthis.context.msBackingStorePixelRatio ||\n", + "\tthis.context.oBackingStorePixelRatio ||\n", + "\tthis.context.backingStorePixelRatio || 1;\n", + "\n", + " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n", + "\n", + " var rubberband = $('');\n", + " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n", + "\n", + " var pass_mouse_events = true;\n", + "\n", + " canvas_div.resizable({\n", + " start: function(event, ui) {\n", + " pass_mouse_events = false;\n", + " },\n", + " resize: function(event, ui) {\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " stop: function(event, ui) {\n", + " pass_mouse_events = true;\n", + " fig.request_resize(ui.size.width, ui.size.height);\n", + " },\n", + " });\n", + "\n", + " function mouse_event_fn(event) {\n", + " if (pass_mouse_events)\n", + " return fig.mouse_event(event, event['data']);\n", + " }\n", + "\n", + " rubberband.mousedown('button_press', mouse_event_fn);\n", + " rubberband.mouseup('button_release', mouse_event_fn);\n", + " // Throttle sequential mouse events to 1 every 20ms.\n", + " rubberband.mousemove('motion_notify', mouse_event_fn);\n", + "\n", + " rubberband.mouseenter('figure_enter', mouse_event_fn);\n", + " rubberband.mouseleave('figure_leave', mouse_event_fn);\n", + "\n", + " canvas_div.on(\"wheel\", function (event) {\n", + " event = event.originalEvent;\n", + " event['data'] = 'scroll'\n", + " if (event.deltaY < 0) {\n", + " event.step = 1;\n", + " } else {\n", + " event.step = -1;\n", + " }\n", + " mouse_event_fn(event);\n", + " });\n", + "\n", + " canvas_div.append(canvas);\n", + " canvas_div.append(rubberband);\n", + "\n", + " this.rubberband = rubberband;\n", + " this.rubberband_canvas = rubberband[0];\n", + " this.rubberband_context = rubberband[0].getContext(\"2d\");\n", + " this.rubberband_context.strokeStyle = \"#000000\";\n", + "\n", + " this._resize_canvas = function(width, height) {\n", + " // Keep the size of the canvas, canvas container, and rubber band\n", + " // canvas in synch.\n", + " canvas_div.css('width', width)\n", + " canvas_div.css('height', height)\n", + "\n", + " canvas.attr('width', width * mpl.ratio);\n", + " canvas.attr('height', height * mpl.ratio);\n", + " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n", + "\n", + " rubberband.attr('width', width);\n", + " rubberband.attr('height', height);\n", + " }\n", + "\n", + " // Set the figure to an initial 600x600px, this will subsequently be updated\n", + " // upon first draw.\n", + " this._resize_canvas(600, 600);\n", + "\n", + " // Disable right mouse context menu.\n", + " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n", + " return false;\n", + " });\n", + "\n", + " function set_focus () {\n", + " canvas.focus();\n", + " canvas_div.focus();\n", + " }\n", + "\n", + " window.setTimeout(set_focus, 100);\n", + "}\n", + "\n", + "mpl.figure.prototype._init_toolbar = function() {\n", + " var fig = this;\n", + "\n", + " var nav_element = $('
    ');\n", + " nav_element.attr('style', 'width: 100%');\n", + " this.root.append(nav_element);\n", + "\n", + " // Define a callback function for later on.\n", + " function toolbar_event(event) {\n", + " return fig.toolbar_button_onclick(event['data']);\n", + " }\n", + " function toolbar_mouse_event(event) {\n", + " return fig.toolbar_button_onmouseover(event['data']);\n", + " }\n", + "\n", + " for(var toolbar_ind in mpl.toolbar_items) {\n", + " var name = mpl.toolbar_items[toolbar_ind][0];\n", + " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", + " var image = mpl.toolbar_items[toolbar_ind][2];\n", + " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", + "\n", + " if (!name) {\n", + " // put a spacer in here.\n", + " continue;\n", + " }\n", + " var button = $('');\n", + " button.click(method_name, toolbar_event);\n", + " button.mouseover(tooltip, toolbar_mouse_event);\n", + " nav_element.append(button);\n", + " }\n", + "\n", + " // Add the status bar.\n", + " var status_bar = $('');\n", + " nav_element.append(status_bar);\n", + " this.message = status_bar[0];\n", + "\n", + " // Add the close button to the window.\n", + " var buttongrp = $('
    ');\n", + " var button = $('');\n", + " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", + " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", + " buttongrp.append(button);\n", + " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", + " titlebar.prepend(buttongrp);\n", + "}\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(el){\n", + " var fig = this\n", + " el.on(\"remove\", function(){\n", + "\tfig.close_ws(fig, {});\n", + " });\n", + "}\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(el){\n", + " // this is important to make the div 'focusable\n", + " el.attr('tabindex', 0)\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " }\n", + " else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._key_event_extra = function(event, name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager)\n", + " manager = IPython.keyboard_manager;\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which == 13) {\n", + " this.canvas_div.blur();\n", + " // select the cell after this one\n", + " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", + " IPython.notebook.select(index + 1);\n", + " }\n", + "}\n", + "\n", + "mpl.figure.prototype.handle_save = function(fig, msg) {\n", + " fig.ondownload(fig, null);\n", + "}\n", + "\n", + "\n", + "mpl.find_output_cell = function(html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] == html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel != null) {\n", + " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plt.figure()\n", + "BLD_w_dwell.N_DWELL.plot.hist(range = (1,50), bins = 49)\n", + "plt.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "application/javascript": [ + "/* Put everything inside the global mpl namespace */\n", + "window.mpl = {};\n", + "\n", + "\n", + "mpl.get_websocket_type = function() {\n", + " if (typeof(WebSocket) !== 'undefined') {\n", + " return WebSocket;\n", + " } else if (typeof(MozWebSocket) !== 'undefined') {\n", + " return MozWebSocket;\n", + " } else {\n", + " alert('Your browser does not have WebSocket support. ' +\n", + " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", + " 'Firefox 4 and 5 are also supported but you ' +\n", + " 'have to enable WebSockets in about:config.');\n", + " };\n", + "}\n", + "\n", + "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", + " this.id = figure_id;\n", + "\n", + " this.ws = websocket;\n", + "\n", + " this.supports_binary = (this.ws.binaryType != undefined);\n", + "\n", + " if (!this.supports_binary) {\n", + " var warnings = document.getElementById(\"mpl-warnings\");\n", + " if (warnings) {\n", + " warnings.style.display = 'block';\n", + " warnings.textContent = (\n", + " \"This browser does not support binary websocket messages. \" +\n", + " \"Performance may be slow.\");\n", + " }\n", + " }\n", + "\n", + " this.imageObj = new Image();\n", + "\n", + " this.context = undefined;\n", + " this.message = undefined;\n", + " this.canvas = undefined;\n", + " this.rubberband_canvas = undefined;\n", + " this.rubberband_context = undefined;\n", + " this.format_dropdown = undefined;\n", + "\n", + " this.image_mode = 'full';\n", + "\n", + " this.root = $('
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    ');\n", + " var titletext = $(\n", + " '
    ');\n", + " titlebar.append(titletext)\n", + " this.root.append(titlebar);\n", + " this.header = titletext[0];\n", + "}\n", + "\n", + "\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._init_canvas = function() {\n", + " var fig = this;\n", + "\n", + " var canvas_div = $('
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    ');\n", + " var button = $('');\n", + " button.click(function (evt) { fig.handle_close(fig, {}); } );\n", + " button.mouseover('Stop Interaction', toolbar_mouse_event);\n", + " buttongrp.append(button);\n", + " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n", + " titlebar.prepend(buttongrp);\n", + "}\n", + "\n", + "mpl.figure.prototype._root_extra_style = function(el){\n", + " var fig = this\n", + " el.on(\"remove\", function(){\n", + "\tfig.close_ws(fig, {});\n", + " });\n", + "}\n", + "\n", + "mpl.figure.prototype._canvas_extra_style = function(el){\n", + " // this is important to make the div 'focusable\n", + " el.attr('tabindex', 0)\n", + " // reach out to IPython and tell the keyboard manager to turn it's self\n", + " // off when our div gets focus\n", + "\n", + " // location in version 3\n", + " if (IPython.notebook.keyboard_manager) {\n", + " IPython.notebook.keyboard_manager.register_events(el);\n", + " }\n", + " else {\n", + " // location in version 2\n", + " IPython.keyboard_manager.register_events(el);\n", + " }\n", + "\n", + "}\n", + "\n", + "mpl.figure.prototype._key_event_extra = function(event, name) {\n", + " var manager = IPython.notebook.keyboard_manager;\n", + " if (!manager)\n", + " manager = IPython.keyboard_manager;\n", + "\n", + " // Check for shift+enter\n", + " if (event.shiftKey && event.which == 13) {\n", + " this.canvas_div.blur();\n", + " // select the cell after this one\n", + " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n", + " IPython.notebook.select(index + 1);\n", + " }\n", + "}\n", + "\n", + "mpl.figure.prototype.handle_save = function(fig, msg) {\n", + " fig.ondownload(fig, null);\n", + "}\n", + "\n", + "\n", + "mpl.find_output_cell = function(html_output) {\n", + " // Return the cell and output element which can be found *uniquely* in the notebook.\n", + " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n", + " // IPython event is triggered only after the cells have been serialised, which for\n", + " // our purposes (turning an active figure into a static one), is too late.\n", + " var cells = IPython.notebook.get_cells();\n", + " var ncells = cells.length;\n", + " for (var i=0; i= 3 moved mimebundle to data attribute of output\n", + " data = data.data;\n", + " }\n", + " if (data['text/html'] == html_output) {\n", + " return [cell, data, j];\n", + " }\n", + " }\n", + " }\n", + " }\n", + "}\n", + "\n", + "// Register the function which deals with the matplotlib target/channel.\n", + "// The kernel may be null if the page has been refreshed.\n", + "if (IPython.notebook.kernel != null) {\n", + " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n", + "}\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plt.figure()\n", + "BLD_w_dwell.dropna(subset = ['N_DWELL']).GKLAS.plot.hist(range = (1110, 1280), bins = 17, label = 'With dwelling info', alpha = 0.7)\n", + "BLD_wo_dwell.GKLAS.plot.hist(range = (1110, 1280), bins = 17, label = 'Without dwelling info', alpha = 0.7)\n", + "plt.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Estimate approximate energy demand\n", + "Only information with entry in \"WHG\", but all \"WHG\" considered - anything with 1 WHG is a SFH, anything with > 1 WHG is a MFH." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "demand_fp = 'D:\\Demand' # '/work/hyenergy/output/demand'\n", + "residential_demand = xr.open_dataset(os.path.join(demand_fp, 'electricity_demand_residential.nc')) " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.Dataset>\n",
+       "Dimensions:        (daytype: 2, timestamp: 288)\n",
+       "Coordinates:\n",
+       "  * timestamp      (timestamp) datetime64[ns] 2001-01-15 ... 2001-12-15T23:00:00\n",
+       "  * daytype        (daytype) object 'weekday' 'weekend'\n",
+       "Data variables:\n",
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+       "    maison         (timestamp, daytype) float64 298.2 344.7 ... 377.5 411.3\n",
+       "    communal_GVA   (timestamp, daytype) float64 241.3 557.0 ... 121.0 302.5\n",
+       "    communal_rest  (timestamp, daytype) float64 79.66 183.9 75.14 ... 39.96 99.9\n",
+       "    weight         (timestamp, daytype) float64 0.0001138 ... 0.0001427\n",
+       "    GVA_annual     float64 2.12e+06\n",
+       "    rest_annual    float64 7e+05
    " + ], + "text/plain": [ + "\n", + "Dimensions: (daytype: 2, timestamp: 288)\n", + "Coordinates:\n", + " * timestamp (timestamp) datetime64[ns] 2001-01-15 ... 2001-12-15T23:00:00\n", + " * daytype (daytype) object 'weekday' 'weekend'\n", + "Data variables:\n", + " appart (timestamp, daytype) float64 ...\n", + " maison (timestamp, daytype) float64 ...\n", + " communal_GVA (timestamp, daytype) float64 ...\n", + " communal_rest (timestamp, daytype) float64 ...\n", + " weight (timestamp, daytype) float64 ...\n", + " GVA_annual float64 ...\n", + " rest_annual float64 ..." + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "residential_demand" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute national statistics" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "BLD_dwell = BLD_w_dwell.dropna(subset = ['N_DWELL'])\n", + "SFH = BLD_dwell[BLD_dwell.N_DWELL == 1]\n", + "MFH = BLD_dwell[BLD_dwell.N_DWELL > 1]" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "residential_demand['N_SFH_CH'] = len(SFH)\n", + "residential_demand['N_DWELL_CH'] = len(DWL[DWL.EGID.isin(MFH.EGID)])\n", + "residential_demand['N_MFH_GE'] = len(MFH[MFH.GDEKT == 'GE'])\n", + "residential_demand['N_MFH_rest'] = len(MFH[MFH.GDEKT != 'GE'])" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "residential_demand['demand_CH'] = ( residential_demand['N_SFH_CH'] * residential_demand['maison'] \n", + " + residential_demand['N_DWELL_CH'] * residential_demand['appart']\n", + " + residential_demand['N_MFH_GE'] * residential_demand['communal_GVA']\n", + " + residential_demand['N_MFH_rest'] * residential_demand['communal_rest']) / 1e9" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
    <xarray.DataArray ()>\n",
+       "array(12818.47281338)
    " + ], + "text/plain": [ + "\n", + "array(12818.47281338)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "get_yearly_sum(residential_demand, var = 'demand_CH')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Save GWR" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    " + ], + "text/plain": [ + " EGID GKODE GKODN GSTAT GKAT GKLAS GGDENR GDEKT \\\n", + "0 1600000 2679684.326 1237449.553 1004.0 1020.0 1122.0 1 ZH \n", + "1 1600001 2679702.150 1237493.958 1004.0 1030.0 1110.0 1 ZH \n", + "2 1600002 2679713.052 1237475.814 1004.0 1030.0 1121.0 1 ZH \n", + "3 1600003 2679655.455 1237516.450 1004.0 1020.0 1122.0 1 ZH \n", + "4 1600005 2679749.269 1237529.346 1004.0 1020.0 1110.0 1 ZH \n", + "\n", + " N_DWELL \n", + "0 3.0 \n", + "1 1.0 \n", + "2 2.0 \n", + "3 3.0 \n", + "4 1.0 " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "BLD_dwell.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "BLD_dwell.to_csv(os.path.join(GWR_fp, 'GWR_GEB_w_N_WHG.csv'), index = False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Define function to get demand of cluster" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "def get_cluster_demand(BLD_df, demand, dwell_nb = 'N_DWELL', canton_id = 'GDEKT', \n", + " SFH_demand = 'maison', MFH_demand = 'appart', \n", + " communal_demand = 'communal_rest', communal_demand_GE = 'communal_GVA'):\n", + "\n", + " SFH = BLD_df[BLD_df[dwell_nb] == 1]\n", + " MFH = BLD_df[BLD_df[dwell_nb] > 1]\n", + " \n", + " building_counts = { 'N_SFH' : len(SFH),\n", + " 'N_DWELL' : MFH[dwell_nb].sum(),\n", + " 'N_MFH' : len(MFH[MFH.GDEKT != 'GE']),\n", + " 'N_MFH_GE': len(MFH[MFH.GDEKT == 'GE']) }\n", + " \n", + " cluster_demand = ( building_counts['N_SFH'] * demand[SFH_demand] \n", + " + building_counts['N_DWELL'] * demand[MFH_demand]\n", + " + building_counts['N_MFH'] * demand[communal_demand] \n", + " + building_counts['N_MFH_GE']* demand[communal_demand_GE] )\n", + " \n", + " return [ cluster_demand, building_counts ]" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "demand, counts = get_cluster_demand(BLD_dwell[BLD_dwell.GDEKT == 'ZH'], residential_demand)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Please read https://msg.pyyaml.org/load for full details.\n", - " data = yaml.load(f.read()) or {}\n", - "/home/walch/.local/lib/python3.6/site-packages/distributed/config.py:20: YAMLLoadWarning: calling yaml.load() without Loader=... is deprecated, as the default Loader is unsafe. Please read https://msg.pyyaml.org/load for full details.\n", - " defaults = yaml.load(f)\n" - ] - } - ], + "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import geopandas as gpd\n", "import xarray as xr\n", "\n", "%matplotlib notebook\n", "import matplotlib.pyplot as plt\n", "\n", "import os\n", "import time\n", "\n", - "import util" + "# import util" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def get_yearly_sum( ds, var = None ):\n", "# create an xarray datasets that contains, for each timestamp in \"ds\", the number of repetitions of that hour in the month\n", "# and sums all values over a year \n", "\n", " days_of_each_month = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]\n", " month = range(1,13)\n", " \n", " days_of_month = pd.DataFrame( data = [ month, days_of_each_month ], index = ['month', 'n_days'] ).transpose()\n", " \n", " timestamps = ds.timestamp.to_dataframe()\n", " timestamps['month'] = timestamps.timestamp.dt.month\n", " \n", " days_of_month = timestamps.merge( days_of_month, on = 'month', how = 'left' ).drop( columns = 'month' ).set_index('timestamp').to_xarray()\n", " \n", " if var is not None:\n", " annual_sum = (ds[var] * days_of_month.n_days).sum( dim = 'timestamp' )\n", " else:\n", " annual_sum = (ds * days_of_month.n_days).sum( dim = 'timestamp' )\n", " \n", " week_weighted = annual_sum.sel( daytype = 'weekday' ) * 5/7 + annual_sum.sel( daytype = 'weekend' ) * 2/7\n", " return week_weighted" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Electricity demand: Apartments, houses and industry\n", "\n", "Voila both normalised service sector values and household values (these are non-normalised)\n", "for weekday/weekend, 12 months for service: banks, commercial offices, schools, hotels, restaurants, hospitals. Also the residential: houses (maison) and appartments with no electric heating nor hot water. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ - "fp_resident = '/work/hyenergy/output/demand/residential_for_alina.csv'\n", - "fp_service = '/work/hyenergy/output/demand/normalised_service_sector_for_alina.csv'" + "fp = 'D:\\Demand' # '/work/hyenergy/output/demand'" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "fp_resident = 'residential_for_alina.csv' \n", + "fp_service = 'normalised_service_sector_for_alina.csv'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Residential sector" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "residential_demand = pd.read_csv( fp_resident, index_col = 0 )" + "residential_demand = pd.read_csv( os.path.join(fp, fp_resident), index_col = 0 )" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "residential_demand['timestamp'] = [pd.to_datetime('2001-%02d-15 %s' %(x.month, x.Time)) \n", " for i,x in residential_demand.iterrows()]" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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    " ], "text/plain": [ " dwelling type month daytype Time Power timestamp\n", "0 appart 1 weekday 00:00:00 162.267134 2001-01-15 00:00:00\n", "1 appart 1 weekday 01:00:00 141.511422 2001-01-15 01:00:00\n", "2 appart 1 weekday 02:00:00 128.611598 2001-01-15 02:00:00\n", "3 appart 1 weekday 03:00:00 127.047450 2001-01-15 03:00:00\n", "4 appart 1 weekday 04:00:00 124.165201 2001-01-15 04:00:00" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "residential_demand.head()" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "residential_pivoted = []\n", "\n", "for cat in residential_demand['dwelling type'].unique():\n", " data = residential_demand[ residential_demand['dwelling type'] == cat ]\n", " residential_pivoted.append(data.set_index(['timestamp', 'daytype']).Power.rename(cat))\n", "residential_pivoted = pd.concat(residential_pivoted, axis = 1)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
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    " ], "text/plain": [ " Category english Month daytype Time Power timestamp\n", "0 BANKS 1 weekday 0 0.025177 2001-01-15 00:00:00\n", "1 BANKS 1 weekday 1 0.025215 2001-01-15 01:00:00\n", "2 BANKS 1 weekday 2 0.026236 2001-01-15 02:00:00\n", "3 BANKS 1 weekday 3 0.027729 2001-01-15 03:00:00\n", "4 BANKS 1 weekday 4 0.031717 2001-01-15 04:00:00" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "service_demand.head()" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "service_pivoted = []\n", "\n", "for cat in service_demand['Category english'].unique():\n", " data = service_demand[ service_demand['Category english'] == cat ]\n", " service_pivoted.append(data.set_index(['timestamp', 'daytype']).Power.rename(cat))\n", "service_pivoted = pd.concat(service_pivoted, axis = 1)" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# service_xr = service_demand.set_index(['Category english', 'timestamp', 'daytype']).to_xarray()[['Power']]\n", "# service_xr = service_xr.rename( {'Category english' : 'category', 'Power' : 'power_norm'} )\n", "service_xr = service_pivoted.to_xarray()" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
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        "    RESTAURANTS         float64 365.0\n",
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    " ], "text/plain": [ "\n", "Dimensions: ()\n", "Data variables:\n", " BANKS float64 365.0\n", " COMMERCIAL OFFICES float64 365.0\n", " HOSPITALS float64 365.0\n", " HOTELS float64 365.0\n", " RESTAURANTS float64 365.0\n", " SCHOOLS float64 365.0" ] }, - "execution_count": 16, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_yearly_sum(service_xr)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Electricity demand: communal areas in buildings\n", "\n", "Hi Alina,\n", "\n", "Yeah sure! \n", "It is called commun d'immeuble and is different for Geneva and rest of the cantons, as GEneva has a policy of having the lights on in the corridors for 24 hours.\n", "Therefore the values I give you is 2.12MWh/app for geneva and 0.7Mwh/app. So if you are modelling a building in Geneca with 50 appartments inside, simply multiply with 2.12 Mwh * 50 for the whole building (MFH), then use the normalised values to construct the load curves for 12 months and 2 day types.\n", "\n", "Let me know if you have any questions.\n", "\n", "Best wishes\n", "Selin" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Get monthly weights" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": { "scrolled": true }, "outputs": [], "source": [ - "fp = '/work/hyenergy/output/demand/commundimmeuble_norm_monthly_for_alina.csv'\n", - "norm_monthly = pd.read_csv(fp, header = None, names = ['month', 'daytype', 'weight'])" + "comm_fp = 'commundimmeuble_norm_monthly_for_alina.csv'\n", + "norm_monthly = pd.read_csv(os.path.join(fp, comm_fp), header = None, names = ['month', 'daytype', 'weight'])" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "GVA_abs = 2.12 * 1e6 # 2.12 MWh/dwelling\n", "rest_abs = 0.70 * 1e6 # 0.7 MWh/dwelling" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2.000000001" ] }, - "execution_count": 19, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "norm_monthly.weight.sum()" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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    " ], "text/plain": [ " month daytype weight\n", "0 1.0 weekday 0.080016\n", "1 1.0 weekend 0.067735\n", "2 2.0 weekday 0.042714\n", "3 2.0 weekend 0.036158\n", "4 3.0 weekday 0.043156" ] }, - "execution_count": 20, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "norm_monthly.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Compute the monthly demand based on the given weights" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "norm_monthly['demand_GVA'] = norm_monthly.weight * GVA_abs\n", "norm_monthly['demand_rest'] = norm_monthly.weight * rest_abs" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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    monthdaytypeweightdemand_GVAdemand_rest
    01.0weekday0.080016169634.4266856011.3673
    11.0weekend0.067735143597.7972047414.3670
    22.0weekday0.04271490554.4389629900.0506
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    \n", "
    " ], "text/plain": [ " month daytype weight demand_GVA demand_rest\n", "0 1.0 weekday 0.080016 169634.42668 56011.3673\n", "1 1.0 weekend 0.067735 143597.79720 47414.3670\n", "2 2.0 weekday 0.042714 90554.43896 29900.0506\n", "3 2.0 weekend 0.036158 76655.53664 25310.7904\n", "4 3.0 weekday 0.043156 91490.65004 30209.1769" ] }, - "execution_count": 22, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "norm_monthly.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Compute the average number of days in each month for weekdays and weekends and get the daily demand" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "dm = pd.Series(data = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31], index = range(1,13))\n", "week_weights = {'weekday' : 5/7, 'weekend' : 2/7}" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "norm_monthly = norm_monthly.merge(dm.rename('dm'), left_on = 'month', right_index = True)\n", "norm_monthly['dm_week'] = norm_monthly.daytype.map(week_weights) * norm_monthly.dm" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "norm_monthly['demand_GVA_day'] = norm_monthly.demand_GVA / norm_monthly.dm_week\n", "norm_monthly['demand_rest_day'] = norm_monthly.demand_rest / norm_monthly.dm_week" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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    monthdaytypeweightdemand_GVAdemand_restdmdm_weekdemand_GVA_daydemand_rest_day
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    " ], "text/plain": [ " month daytype weight demand_GVA demand_rest dm dm_week \\\n", "0 1.0 weekday 0.080016 169634.42668 56011.3673 31 22.142857 \n", "1 1.0 weekend 0.067735 143597.79720 47414.3670 31 8.857143 \n", "2 2.0 weekday 0.042714 90554.43896 29900.0506 28 20.000000 \n", "3 2.0 weekend 0.036158 76655.53664 25310.7904 28 8.000000 \n", "4 3.0 weekday 0.043156 91490.65004 30209.1769 31 22.142857 \n", "\n", " demand_GVA_day demand_rest_day \n", "0 7660.909592 2529.545620 \n", "1 16212.654523 5353.234984 \n", "2 4527.721948 1495.002530 \n", "3 9581.942080 3163.848800 \n", "4 4131.835808 1364.285408 " ] }, - "execution_count": 26, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "norm_monthly.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load the hourly weights" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ - "fp = '/work/hyenergy/output/demand/commundimmeuble_norm_for_alina.csv'\n", - "norm_hourly = pd.read_csv(fp)" + "comm_fp = 'commundimmeuble_norm_for_alina.csv' \n", + "norm_hourly = pd.read_csv(os.path.join(fp, comm_fp))" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "norm_hourly['hour'] = norm_hourly.hour - 1\n", "norm_hourly['timestamp'] = [pd.to_datetime('2001-%02d-15 %02d:00' %(x.month, x.hour)) \n", " for i,x in norm_hourly.iterrows()]" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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    monthdaytypehour16timestamp
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    11weekday10.0297062001-01-15 01:00:00
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    " ], "text/plain": [ " month daytype hour 16 timestamp\n", "0 1 weekday 0 0.031492 2001-01-15 00:00:00\n", "1 1 weekday 1 0.029706 2001-01-15 01:00:00\n", "2 1 weekday 2 0.029706 2001-01-15 02:00:00\n", "3 1 weekday 3 0.029706 2001-01-15 03:00:00\n", "4 1 weekday 4 0.029706 2001-01-15 04:00:00" ] }, - "execution_count": 29, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "norm_hourly.head()" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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    hour16
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    " ], "text/plain": [ " hour 16\n", "month daytype \n", "1 weekday 276 1.0\n", " weekend 276 1.0\n", "2 weekday 276 1.0\n", " weekend 276 1.0\n", "3 weekday 276 1.0" ] }, - "execution_count": 30, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "norm_hourly.groupby(['month', 'daytype']).sum().head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Merge the daily demand" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "norm_merged = norm_hourly.merge(norm_monthly[['month', 'daytype', 'demand_GVA_day', 'demand_rest_day']], \n", " on = ['month', 'daytype'])" ] }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "norm_merged['communal_GVA'] = norm_merged.demand_GVA_day * norm_merged['16']\n", "norm_merged['communal_rest'] = norm_merged.demand_rest_day * norm_merged['16']" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "norm_merged['weight'] = norm_merged.communal_rest / rest_abs" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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    monthdaytypehour16timestampdemand_GVA_daydemand_rest_daycommunal_GVAcommunal_restweight
    01weekday00.0314922001-01-15 00:00:007660.9095922529.54562241.26015379.6613710.000114
    11weekday10.0297062001-01-15 01:00:007660.9095922529.54562227.57334175.1421410.000107
    21weekday20.0297062001-01-15 02:00:007660.9095922529.54562227.57334175.1421410.000107
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    41weekday40.0297062001-01-15 04:00:007660.9095922529.54562227.57334175.1421410.000107
    \n", "
    " ], "text/plain": [ " month daytype hour 16 timestamp demand_GVA_day \\\n", "0 1 weekday 0 0.031492 2001-01-15 00:00:00 7660.909592 \n", "1 1 weekday 1 0.029706 2001-01-15 01:00:00 7660.909592 \n", "2 1 weekday 2 0.029706 2001-01-15 02:00:00 7660.909592 \n", "3 1 weekday 3 0.029706 2001-01-15 03:00:00 7660.909592 \n", "4 1 weekday 4 0.029706 2001-01-15 04:00:00 7660.909592 \n", "\n", " demand_rest_day communal_GVA communal_rest weight \n", "0 2529.54562 241.260153 79.661371 0.000114 \n", "1 2529.54562 227.573341 75.142141 0.000107 \n", "2 2529.54562 227.573341 75.142141 0.000107 \n", "3 2529.54562 227.573341 75.142141 0.000107 \n", "4 2529.54562 227.573341 75.142141 0.000107 " ] }, - "execution_count": 34, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "norm_merged.head()" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
    \n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
    timestampdaytypecategorypower_W
    02001-01-15 00:00:00weekdaycommunal_GVA241.260153
    12001-01-15 01:00:00weekdaycommunal_GVA227.573341
    22001-01-15 02:00:00weekdaycommunal_GVA227.573341
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    42001-01-15 04:00:00weekdaycommunal_GVA227.573341
    \n", "
    " ], "text/plain": [ " timestamp daytype category power_W\n", "0 2001-01-15 00:00:00 weekday communal_GVA 241.260153\n", "1 2001-01-15 01:00:00 weekday communal_GVA 227.573341\n", "2 2001-01-15 02:00:00 weekday communal_GVA 227.573341\n", "3 2001-01-15 03:00:00 weekday communal_GVA 227.573341\n", "4 2001-01-15 04:00:00 weekday communal_GVA 227.573341" ] }, - "execution_count": 35, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "communal_W = norm_merged.melt( id_vars = ['timestamp', 'daytype'], value_vars = ['communal_GVA', 'communal_rest', 'weight'], \n", " var_name = 'category', value_name = 'power_W')\n", "communal_W.head()" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "comm_pivoted = []\n", "\n", "for cat in communal_W['category'].unique():\n", " data = communal_W[ communal_W['category'] == cat ]\n", " comm_pivoted.append(data.set_index(['timestamp', 'daytype']).power_W.rename(cat))\n", "comm_pivoted = pd.concat(comm_pivoted, axis = 1)" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
    <xarray.Dataset>\n",
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        "Dimensions:        (daytype: 2, timestamp: 288)\n",
        "Coordinates:\n",
        "  * timestamp      (timestamp) datetime64[ns] 2001-01-15 ... 2001-12-15T23:00:00\n",
        "  * daytype        (daytype) object 'weekday' 'weekend'\n",
        "Data variables:\n",
        "    communal_GVA   (timestamp, daytype) float64 241.3 557.0 ... 121.0 302.5\n",
        "    communal_rest  (timestamp, daytype) float64 79.66 183.9 75.14 ... 39.96 99.9\n",
        "    weight         (timestamp, daytype) float64 0.0001138 ... 0.0001427\n",
        "    GVA_annual     float64 2.12e+06\n",
-       "    rest_annual    float64 7e+05
    "
+       "    rest_annual    float64 7e+05
    " ], "text/plain": [ "\n", "Dimensions: (daytype: 2, timestamp: 288)\n", "Coordinates:\n", " * timestamp (timestamp) datetime64[ns] 2001-01-15 ... 2001-12-15T23:00:00\n", " * daytype (daytype) object 'weekday' 'weekend'\n", "Data variables:\n", " communal_GVA (timestamp, daytype) float64 241.3 557.0 ... 121.0 302.5\n", " communal_rest (timestamp, daytype) float64 79.66 183.9 75.14 ... 39.96 99.9\n", " weight (timestamp, daytype) float64 0.0001138 ... 0.0001427\n", " GVA_annual float64 2.12e+06\n", " rest_annual float64 7e+05" ] }, - "execution_count": 37, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# communal_xr = communal_W.set_index(['category', 'timestamp', 'daytype']).to_xarray()\n", "communal_xr = comm_pivoted.to_xarray()\n", "communal_xr['GVA_annual' ] = GVA_abs\n", "communal_xr['rest_annual'] = rest_abs\n", "communal_xr" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 39, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ - "
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    <xarray.Dataset>\n",
        "Dimensions:        ()\n",
        "Data variables:\n",
        "    communal_GVA   float64 2.12e+06\n",
        "    communal_rest  float64 7e+05\n",
        "    weight         float64 1.0\n",
        "    GVA_annual     float64 1.857e+10\n",
-       "    rest_annual    float64 6.132e+09
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+       "    rest_annual    float64 6.132e+09
    " ], "text/plain": [ "\n", "Dimensions: ()\n", "Data variables:\n", " communal_GVA float64 2.12e+06\n", " communal_rest float64 7e+05\n", " weight float64 1.0\n", " GVA_annual float64 1.857e+10\n", " rest_annual float64 6.132e+09" ] }, - "execution_count": 38, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_yearly_sum(communal_xr)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Merge information and save" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "residential_xr = xr.merge([residential_xr, communal_xr])" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
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        "Dimensions:        (daytype: 2, timestamp: 288)\n",
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        "  * timestamp      (timestamp) datetime64[ns] 2001-01-15 ... 2001-12-15T23:00:00\n",
        "  * daytype        (daytype) object 'weekday' 'weekend'\n",
        "Data variables:\n",
        "    appart         (timestamp, daytype) float64 162.3 181.7 ... 233.7 227.8\n",
        "    maison         (timestamp, daytype) float64 298.2 344.7 ... 377.5 411.3\n",
        "    communal_GVA   (timestamp, daytype) float64 241.3 557.0 ... 121.0 302.5\n",
        "    communal_rest  (timestamp, daytype) float64 79.66 183.9 75.14 ... 39.96 99.9\n",
        "    weight         (timestamp, daytype) float64 0.0001138 ... 0.0001427\n",
        "    GVA_annual     float64 2.12e+06\n",
-       "    rest_annual    float64 7e+05
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    " ], "text/plain": [ "\n", "Dimensions: (daytype: 2, timestamp: 288)\n", "Coordinates:\n", " * timestamp (timestamp) datetime64[ns] 2001-01-15 ... 2001-12-15T23:00:00\n", " * daytype (daytype) object 'weekday' 'weekend'\n", "Data variables:\n", " appart (timestamp, daytype) float64 162.3 181.7 ... 233.7 227.8\n", " maison (timestamp, daytype) float64 298.2 344.7 ... 377.5 411.3\n", " communal_GVA (timestamp, daytype) float64 241.3 557.0 ... 121.0 302.5\n", " communal_rest (timestamp, daytype) float64 79.66 183.9 75.14 ... 39.96 99.9\n", " weight (timestamp, daytype) float64 0.0001138 ... 0.0001427\n", " GVA_annual float64 2.12e+06\n", " rest_annual float64 7e+05" ] }, - "execution_count": 40, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "residential_xr" ] }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ - "residential_xr.to_netcdf('/work/hyenergy/output/demand/electricity_demand_residential.nc')" + "residential_xr.to_netcdf(os.path.join(fp, 'electricity_demand_residential.nc')) " ] }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ - "service_xr.to_netcdf('/work/hyenergy/output/demand/electricity_demand_services.nc')" + "service_xr.to_netcdf(os.path.join(fp, 'electricity_demand_services.nc'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plot results" ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "import calendar" ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "residential_plot = residential_xr.copy()" ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "residential_plot.coords['timestamp'] = (residential_plot.timestamp.dt.month-1) * 24 + residential_plot.timestamp.dt.hour" ] }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
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+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
    <xarray.Dataset>\n",
        "Dimensions:        (daytype: 2, timestamp: 288)\n",
        "Coordinates:\n",
        "  * timestamp      (timestamp) int64 0 1 2 3 4 5 6 ... 282 283 284 285 286 287\n",
        "  * daytype        (daytype) object 'weekday' 'weekend'\n",
        "Data variables:\n",
        "    appart         (timestamp, daytype) float64 162.3 181.7 ... 233.7 227.8\n",
        "    maison         (timestamp, daytype) float64 298.2 344.7 ... 377.5 411.3\n",
        "    communal_GVA   (timestamp, daytype) float64 241.3 557.0 ... 121.0 302.5\n",
        "    communal_rest  (timestamp, daytype) float64 79.66 183.9 75.14 ... 39.96 99.9\n",
        "    weight         (timestamp, daytype) float64 0.0001138 ... 0.0001427\n",
        "    GVA_annual     float64 2.12e+06\n",
-       "    rest_annual    float64 7e+05
    "
+       "    rest_annual    float64 7e+05
    " ], "text/plain": [ "\n", "Dimensions: (daytype: 2, timestamp: 288)\n", "Coordinates:\n", " * timestamp (timestamp) int64 0 1 2 3 4 5 6 ... 282 283 284 285 286 287\n", " * daytype (daytype) object 'weekday' 'weekend'\n", "Data variables:\n", " appart (timestamp, daytype) float64 162.3 181.7 ... 233.7 227.8\n", " maison (timestamp, daytype) float64 298.2 344.7 ... 377.5 411.3\n", " communal_GVA (timestamp, daytype) float64 241.3 557.0 ... 121.0 302.5\n", " communal_rest (timestamp, daytype) float64 79.66 183.9 75.14 ... 39.96 99.9\n", " weight (timestamp, daytype) float64 0.0001138 ... 0.0001427\n", " GVA_annual float64 2.12e+06\n", " rest_annual float64 7e+05" ] }, - "execution_count": 46, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "residential_plot" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 48, "metadata": { "scrolled": false }, "outputs": [ { "data": { "application/javascript": [ "/* Put everything inside the global mpl namespace */\n", "window.mpl = {};\n", "\n", "\n", "mpl.get_websocket_type = function() {\n", " if (typeof(WebSocket) !== 'undefined') {\n", " return WebSocket;\n", " } else if (typeof(MozWebSocket) !== 'undefined') {\n", " return MozWebSocket;\n", " } else {\n", " alert('Your browser does not have WebSocket support. ' +\n", " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n", " 'Firefox 4 and 5 are also supported but you ' +\n", " 'have to enable WebSockets in about:config.');\n", " };\n", "}\n", "\n", "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n", " this.id = figure_id;\n", "\n", " this.ws = websocket;\n", "\n", " this.supports_binary = (this.ws.binaryType != undefined);\n", "\n", " if (!this.supports_binary) {\n", " var warnings = document.getElementById(\"mpl-warnings\");\n", " if (warnings) {\n", " warnings.style.display = 'block';\n", " warnings.textContent = (\n", " \"This browser does not support binary websocket messages. \" +\n", " \"Performance may be slow.\");\n", " }\n", " }\n", "\n", " this.imageObj = new Image();\n", "\n", " this.context = undefined;\n", " this.message = undefined;\n", " this.canvas = undefined;\n", " this.rubberband_canvas = undefined;\n", " this.rubberband_context = undefined;\n", " this.format_dropdown = undefined;\n", "\n", " this.image_mode = 'full';\n", "\n", " this.root = $('
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    ');\n", " var titletext = $(\n", " '
    ');\n", " titlebar.append(titletext)\n", " this.root.append(titlebar);\n", " this.header = titletext[0];\n", "}\n", "\n", "\n", "\n", "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "\n", "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n", "\n", "}\n", "\n", "mpl.figure.prototype._init_canvas = function() {\n", " var fig = this;\n", "\n", " var canvas_div = $('
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    ');\n", " nav_element.attr('style', 'width: 100%');\n", " this.root.append(nav_element);\n", "\n", " // Define a callback function for later on.\n", " function toolbar_event(event) {\n", " return fig.toolbar_button_onclick(event['data']);\n", " }\n", " function toolbar_mouse_event(event) {\n", " return fig.toolbar_button_onmouseover(event['data']);\n", " }\n", "\n", " for(var toolbar_ind in mpl.toolbar_items) {\n", " var name = mpl.toolbar_items[toolbar_ind][0];\n", " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n", " var image = mpl.toolbar_items[toolbar_ind][2];\n", " var method_name = mpl.toolbar_items[toolbar_ind][3];\n", "\n", " if (!name) {\n", " // put a spacer in here.\n", " continue;\n", " }\n", " var button = $('