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solarPOT/PV_output/deprecated/compute_rooftop_irrad.py
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compute_rooftop_irrad.py
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import pandas as pd
import xarray as xr
import numpy as np
import os
import sys
import time
## for more details, check reference in Total_radiation_spielwiese
## ===== INPUT DATA ===== ##
PARTLY_SHADING_FP = 'CH_partly_shading_replaced.nc' # '/work/hyenergy/output/solar_potential/geographic_potential/available_area/shading_stats/CH_partly_shading_replaced.nc'
TILTED_IRRAD_FP = sys.argv[1]
OUTFILE_FP = sys.argv[2]
SVF = 0.9 # standard value for sky-view-factor
## =========================================================================================== ##
## ===== LOAD DATA ===== ##
tt = time.time()
t0 = time.time()
shading = xr.open_dataset( PARTLY_SHADING_FP )
irrad = xr.open_dataset( TILTED_IRRAD_FP )
print('Loaded all data in %.3f seconds' %(time.time()-tt))
## ===== COMPUTE MEAN IRRADIANCE ON ROOFTOPS ===== ##
shading_select = shading.sel( DF_UID = irrad.DF_UID )
irrad_select = irrad.sel( timestamp = shading_select.timestamp )
# Align types of coordinates
shading_select = shading_select.assign_coords( DF_UID = shading_select.DF_UID.astype(int) ).isel(DF_UID = range(1000))
irrad_select = irrad_select.assign_coords( DF_UID = irrad_select.DF_UID.astype(int) ).isel(DF_UID = range(1000))
print(shading_select)
print(irrad_select)
tt = time.time()
tilted_irradiance = shading_select.partly_shading * irrad_select.poa_direct + SVF * irrad_select.poa_sky_diffuse + irrad_select.poa_ground_diffuse
tilted_irrad = tilted_irradiance.to_dataset( name = 'tilted_irradiance' )
print('Computed tilted irradiance in %.3f seconds' %(time.time() - tt))
print(tilted_irrad)
print('you have 20s to cancel')
time.sleep(20)
tilted_irrad['svf'] = SVF
print('Computed mean irradiance on rooftops')
## ===== GET DAYS OF EACH MONTH ===== ##
# NOTE: this is necessary in order to be able to compute correct yearly and monthly sums
days_of_each_month = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
month = range(1,13)
# first get dataframe, then merge it to get to required timestamps
days_of_month = pd.DataFrame( data = [ month, days_of_each_month ], index = ['month', 'n_days'] ).transpose()
timestamps = tilted_irrad.timestamp.to_dataframe()
timestamps['month'] = timestamps.timestamp.dt.month
days_of_month = timestamps.merge( days_of_month, on = 'month', how = 'left' ).drop( columns = 'month' ).set_index('timestamp').to_xarray()
print('Computed days of each month')
# First merge all required variables for computation of PV output
rooftop_irrad = xr.merge( [tilted_irrad.tilted_irradiance, days_of_month ])
# Compute yearly sums
rooftop_irrad['yearly_kWh_m2_a'] = (rooftop_irrad.tilted_irradiance * rooftop_irrad.n_days).sum( dim = 'timestamp' ) / 1000
print('Computed yearly sum')
rooftop_irrad.to_netcdf( OUTFILE_FP )
print('Saved output in %s' %OUTFILE_FP)
print('DONE - total execution time: %.3f seconds' %(time.time()-tt))
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