Homec4science
Diffusion solar_potential (aw_solar) Browse

solarPOT/PV_output/deprecated/compute_roof_irrad_loop.py
b2056863329baw_solar

Last Change
Raw File

compute_roof_irrad_loop.py
View Options

import pandas as pd
import xarray as xr
import numpy as np
import sys
import os
import time
## for more details, check reference in Total_radiation_spielwiese
## ===== INPUT DATA ===== ##
BATCH_SIZE = 10000
PARTLY_SHADING_FP = '/scratch/walch/workspace_tilted_irrad_change/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_BASE = sys.argv[2]
SVF = 0.9 # standard value for sky-view-factor
## =========================================================================================== ##
## ===== LOAD DATA ===== ##
tt = 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))
shading = shading.sel( DF_UID = irrad.DF_UID )
irrad = irrad.sel( timestamp = shading.timestamp )
shading.to_netcdf( 'shading_select.nc' )
## ===== 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 = 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')
## ==================== ##
## ===== FOR-LOOP ===== ##
N_BATCHES = int( len( shading.DF_UID ) / BATCH_SIZE ) + 1
for i in range( N_BATCHES ):
print('Current batch: %d' %i)
tt = time.time()
start = i * BATCH_SIZE
end = min( (i+1) * BATCH_SIZE, len( shading.DF_UID ) )
## ===== COMPUTE MEAN IRRADIANCE ON ROOFTOPS ===== ##
subset_shade = shading.isel( DF_UID = range( start, end ) ).transpose()
subset_irrad = irrad.isel( DF_UID = range( start, end ) )
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))
tilted_irrad['svf'] = SVF
## ===== PREPARE PV OUTPUT ===== ##
# 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_BASE + ('_%d.nc' %i) )
print('Saved output in %s_%d.nc' %(OUTFILE_BASE, i))
print('Batch execution time: %.3f seconds' %(time.time()-tt))
print('DONE')
c4science · Help