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solarPOT/Tilted_irradiance/tilted_irradiance_components.py
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tilted_irradiance_components.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 beam irradiance for entire dataframe
tt = util.Timer()
beam_component = pvlib.beam_component(
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'],
)
print('\nComputed beam irradiance component')
tt.stop()
### Compute the sky diffuse irradiance components for entire dataframe
tt = util.Timer()
sky_diffuse_perez = pvlib.perez(
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'],
dhi = merged_dataset['DHI'],
dni_extra = merged_dataset['DNI_extra'],
airmass = merged_dataset['rel_airmass'],
return_components = True
)
# rename for consistency with previous version
sky_diffuse_perez = sky_diffuse_perez.rename({'sky_diffuse' : 'poa_sky_diffuse',
'isotropic' : 'poa_sky_isotropic',
'circumsolar' : 'poa_sky_circumsolar',
'horizon' : 'poa_sky_horizon'}, axis = 1)
print('\nComputed perez diffuse irradiance components')
tt.stop()
### Compute ground reflected irradiance for entire dataframe
tt = util.Timer()
ground_diffuse = pvlib.get_ground_diffuse(
surface_tilt = merged_dataset['panel_tilt'],
ghi = merged_dataset['SIS'],
albedo = albedo_input[albedo_in](merged_dataset),
)
print('\nComputed ground reflected irradiance component')
tt.stop()
### Merge and postprocess the data
irradiance_components = pd.concat([beam_component.rename('poa_direct'),
sky_diffuse_perez,
ground_diffuse.rename('poa_ground_diffuse')], axis = 1)
# 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)
print('\nAdded columns for unique identification to dataframe')
tt.stop()
tt = util.Timer()
#irradiance_xr = util.to_xarray( tilted_irradiance, ['timestamp', 'DF_UID'] )
#print('Converted dataset to xarray')
#tt.restart()
#irradiance_xr.to_netcdf( savefile )
tilted_irradiance.to_csv( savefile )
# --> 2GB on disk
print('\nSaved file as %s' %savefile)
tt.stop()
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