Homec4science
Diffusion solar_potential (aw_solar) Browse

solarPOT/EW_scenarios_MMH/merge_annual_data.py
29f993e4425aaw_solar

Last Change
Raw File

merge_annual_data.py
View Options

import numpy as np
import pandas as pd
import xarray as xr
import sys
import os
import time
# # Define file paths
COMM_FP = '/work/hyenergy/output/solar_potential/Sonnendach_Gemeinden_Dachflaechen.csv'
PV_PATH = sys.argv[1]
ROOF_FP = sys.argv[2]
AREA_FP = sys.argv[3]
OUTPATH = sys.argv[4]
## CONSTANTS
GT_IN = 'tilted_irrad_*.nc'
PV_IN = 'pv_potential_*.nc'
GT_OUT = 'tilted_irrad.nc'
PV_OUT = 'pv_potential.nc'
FLAT_TILT = 10
MAX_ASPECT = 90
# Print file paths
print('\nCommune mapping file path: %s' %COMM_FP)
print('Available area file path: %s' %AREA_FP)
print('Roof surfaces file path: %s' %ROOF_FP)
print('PV and irradiance input path: %s' %PV_PATH)
print('Output file: %s\n\n' %OUTPATH)
# # Load commune map
tt = time.time()
comm_map = pd.read_csv(COMM_FP, encoding = 'cp1250', usecols = ['DF_UID', 'BFS_NUMMER', 'NAME'] )
# # Load roof data
roofs = pd.read_csv(ROOF_FP, usecols = ['DF_UID', 'NEIGUNG', 'AUSRICHTUNG'])
av_area = pd.read_csv(AREA_FP, usecols = ['DF_UID', 'available_area'])
roof_info = roofs.merge(av_area, on = 'DF_UID').merge(comm_map, on = 'DF_UID', how = 'left')
print('Loaded roof info in %.2fs' %(time.time()-tt))
# # Get suitable roofs
roof_info['suitable'] = ( ( (roof_info.NEIGUNG <= FLAT_TILT) | (abs(roof_info.AUSRICHTUNG) <= MAX_ASPECT) )
& (roof_info.available_area >= 8) ).astype(int)
# # Load prediction
tilted_irrad = xr.open_mfdataset( os.path.join(PV_PATH, GT_IN) )
pv_potential = xr.open_mfdataset( os.path.join(PV_PATH, PV_IN) )
# Postprocess instead of weird merging
if 'timestamp' in tilted_irrad.yearly_kWh_m2.coords:
tilted_irrad['yearly_kWh_m2'] = tilted_irrad['yearly_kWh_m2'].isel( timestamp = 0 )
if 'timestamp' in pv_potential.yearly_PV_kWh.coords:
pv_potential['yearly_PV_kWh'] = pv_potential['yearly_PV_kWh'].isel( timestamp = 0 )
print(tilted_irrad)
print(pv_potential)
tt = time.time()
tilted_irrad.to_netcdf( os.path.join(PV_PATH, GT_OUT) )
pv_potential.to_netcdf( os.path.join(PV_PATH, PV_OUT) )
print('Saved hourly PV and radiation data in %.2fs\n' %(time.time()-tt))
tt = time.time()
roof_info = roof_info.merge( tilted_irrad.yearly_kWh_m2.to_series().rename('Gt_kWh_m2'), on = 'DF_UID' )
roof_info = roof_info.merge( pv_potential.yearly_PV_kWh.to_series().rename('EPV_kWh'), on = 'DF_UID' )
print('Processed annual PV data in %.2fs\n' %(time.time()-tt))
# ### Save rooftop data
tt = time.time()
roof_save = roof_info.drop(columns = ['NEIGUNG', 'AUSRICHTUNG', 'NAME'])
print(roof_save.head())
roof_save.to_csv( OUTPATH, index = False )
print('\nSaved annual PV data in %.2fs' %(time.time()-tt))
c4science · Help