Diffusion solar_potential (aw_solar) Browse

poa_irradiance.py
b2056863329baw_solar

poa_irradiance.py
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			import numpy as np
			import pandas as pd
			import xarray as xr
			import os
			import sys
			import util
			import pvlib.irradiance as pvlib
			import rooftop_handling

			def reindex_dataframe(input_array):
			# function takes xarray and reindexes x and y coordinates to x and y indices
			array_reindexed = input_array
			array_reindexed.coords['x'] = range(len(array_reindexed.x))
			array_reindexed.coords['y'] = range(len(array_reindexed.y))

			return array_reindexed.rename({'x' : 'x_idx', 'y' : 'y_idx'})

			def merge_ALB_on_meteo(albedo_df, meteo_df):
			# function takes two dataframes of albedo and meteo and matches based on month, x_idx. y_idx
			meteo_df['month'] = meteo_df['timestamp'].dt.month
			return meteo_df.merge(albedo_df, on = ['x_idx', 'y_idx', 'month']).drop('month', axis = 1)

			##################### INPUTS ####################

			meteo_file = sys.argv[1]
			print('Meteo data loaded from %s' %meteo_file)

			rooftop_file = sys.argv[2]
			print('Rooftop data loaded from %s' %rooftop_file)

			savefile = sys.argv[3]
			print('Data will be saved to %s' %savefile)

			if len(sys.argv) > 4:
			albedo_file = sys.argv[4]
			albedo_in = 'map'
			print('Albedo data loaded from %s' %albedo_file)

			else:
			albedo_file = None
			albedo_in = 'constant'

			if len(sys.argv) > 6:
			start_index = int(sys.argv[5])
			n_rows = int(sys.argv[6])
			get_slice = True
			print('Loading slice: %d rows, starting from ID %d' %(n_rows, start_index))
			else:
			get_slice = False
			start_index = 1
			n_rows = None

			##################### CONSTANTS ##########################

			ROOFTOP_FEATURES = ['XCOORD', 'YCOORD', 'DF_UID', 'FLAECHE', 'panel_tilt', 'panel_direction']
			UNIQUE_FEATURE = 'DF_UID'
			DIFFUSE_MODEL = 'perez'

			albedo_input = {
			'constant' : lambda ds : 0.2 ,
			'map' : lambda ds : ds['ALB']
			}

			################################################

			# Create dataframe of rooftops
			field_names = pd.read_csv(rooftop_file, nrows = 0).columns
			rooftops_all = pd.read_csv(rooftop_file, skiprows = start_index, nrows = n_rows, header = None, names = field_names)
			rooftops = rooftops_all.loc[:, ROOFTOP_FEATURES]

			# Open datasets
			meteo = xr.open_dataset( meteo_file )
			# if only a part of the data is queried, use only the subset of the meteo data in that range, for efficiency reasons
			if get_slice:
			meteo = meteo.sel( x = slice( rooftops.XCOORD.min()-200, rooftops.XCOORD.max()+200), y = slice( rooftops.YCOORD.min()-200, rooftops.YCOORD.max()+200) )

			if albedo_file is not None:
			albedo = xr.open_dataset( albedo_file )
			if get_slice:
			albedo = albedo.sel( x = slice( rooftops.XCOORD.min()-200, rooftops.XCOORD.max()+200), y = slice( rooftops.YCOORD.min()-200, rooftops.YCOORD.max()+200) )

			# link rooftops with meteo pixels
			rooftops_linked = rooftop_handling.link_rooftops_with_pixels(rooftops, meteo)

			# reindex meteo data from coordinates to indices
			meteo_reindexed = reindex_dataframe(meteo)

			# convert xarray to dataframe
			meteo_df = util.to_pandas(meteo_reindexed)

			# if an albedo was given, merge a column of ALB to meteo_df
			if albedo_file is not None:
			albedo_reindexed = reindex_dataframe(albedo.ALB)
			albedo_df = util.to_pandas(albedo_reindexed)

			meteo_df = merge_ALB_on_meteo( albedo_df, meteo_df )

			# merge rooftops with meteo data
			tt = util.Timer()
			merged_dataset = rooftops_linked.merge(meteo_df, on = ['x_idx', 'y_idx'])

			print('\nMerged meteo data on rooftop dataset')
			tt.stop()

			tmp_fp = os.path.join( os.path.split( savefile )[0], 'TMP_merged_dataset.csv')
			merged_dataset.to_csv( tmp_fp )
			print('Saved temporary merged dataset in %s' %tmp_fp)
			exit()

			################################################

			# Compute tilted irradiance for entire dataframe
			tt = util.Timer()
			irradiance_components = pvlib.get_total_irradiance(
			surface_tilt = merged_dataset['panel_tilt'],
			surface_azimuth = merged_dataset['panel_direction'],
			solar_zenith = merged_dataset['apparent_zenith'],
			solar_azimuth = merged_dataset['azimuth'],
			dni = merged_dataset['DNI'],
			ghi = merged_dataset['SIS'],
			dhi = merged_dataset['DHI'],
			dni_extra = merged_dataset['DNI_extra'],
			airmass = merged_dataset['rel_airmass'],
			albedo = albedo_input[albedo_in](merged_dataset),
			model = DIFFUSE_MODEL
			)

			print('\nComputed irradiance components')
			tt.stop()

			# add UNIQUE_FEATURE and timestamp to the tilted irradiance and drop data that can be calculated (global and diffuse)
			tt = util.Timer()
			tilted_irradiance_features = [ UNIQUE_FEATURE, 'timestamp' ]
			tilted_irradiance = pd.concat([ merged_dataset.loc[: ,tilted_irradiance_features], irradiance_components], axis = 1)
			tilted_irradiance.drop(['poa_global' , 'poa_diffuse'], axis = 1, inplace = True)

			print('\nAdded columns for unique identification to dataframe')
			tt.stop()

			### Convert pandas dataframe back to xarray to save in a more efficient format
			tt = util.Timer()
			indexed_data = tilted_irradiance.set_index(['DF_UID', 'timestamp'])
			print('Indexed data')
			tt.stop()

			tt = util.Timer()
			data_xr = indexed_data.to_xarray()
			print('Coverted to xarray ')
			tt.stop()

			# change type of "timestamp"
			timestamp_str = data_xr.timestamp
			data_dt = data_xr.assign_coords( timestamp = pd.to_datetime(timestamp_str) )

			# save output
			tt = util.Timer()
			data_dt.to_netcdf( savefile )
			print('\nSaved file as %s' %savefile)
			tt.stop()

solarPOT/Hourly_PV_public/Geographic_potential/Tilted_radiation/poa_irradiance.pyb2056863329baw_solar

poa_irradiance.pyView Options

solarPOT/Hourly_PV_public/Geographic_potential/Tilted_radiation/poa_irradiance.py
b2056863329baw_solar

poa_irradiance.py
View Options