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solarPOT/GVA_SCENARIOS/efficiency_mapping.py
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efficiency_mapping.py
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import numpy as np
import xarray as xr
import pandas as pd
from pvlib import pvsystem
import os
import time
import util
import sys
## === SPECIFY FILE PATHS === ##
NAME = sys.argv[1]
WORKDIR = '/scratch/walch/scenarios_GVA'
tilted_irrad_fp = os.path.join( WORKDIR, 'tilted_irrad_GVA_flat_%s.nc' %NAME ) # TILTED RADIATION FILE PATH
performance_fp = '/work/hyenergy/output/solar_potential/technical_potential/pv_efficiencies.nc' # PANEL PERFORMANCE FILE PATH
T_max_mmd_fp = '/work/hyenergy/output/solar_potential/technical_potential/Tmax_surface_mmd.nc' # MAX TEMPERATURE MMD FILE PATH
ROOFS_fp = os.path.join( WORKDIR, 'flat_roofs_GVA_%s.csv' %NAME ) # ROOFTOP CHARACTERISTICS FILE PATH
OUTFILE = os.path.join( WORKDIR, 'efficiency_mapped_GVA_flat_%s.nc' %NAME )# FILE PATH FOR OUTPUT FILE
## === LOAD DATA === ##
tilted_irrad = xr.open_mfdataset( tilted_irrad_fp, chunks = {'DF_UID' : 100000} )
performance = xr.open_dataset( performance_fp )
T_max_mmd = xr.open_dataset( T_max_mmd_fp )
roofs = pd.read_csv(ROOFS_fp, usecols = ['DF_UID', 'XCOORD', 'YCOORD'])
OUTPATH = os.path.split( OUTFILE )[0]
try:
os.mkdir( os.path.join(OUTPATH, 'tmp') )
except:
print('tmp directory already exists')
# get pixel coordinates of roofs, by rounding to the coordinate of the closest pixel
roofs['chx'] = util.round_to_interval(roofs['XCOORD'] - 500, 1000) + 500
roofs['chy'] = util.round_to_interval(roofs['YCOORD'] - 500, 1000) + 500
## ==== MAP PANEL PERFORMANCE TO ROOFS ==== ##
month_old = 0
# treat every timestamp independently
for i, t in enumerate(tilted_irrad.timestamp):
tt = time.time()
# get month for maximum temperature
month = int(t.dt.month)
# get tilted irradiance for given timestamp
Gt = tilted_irrad.sel(timestamp = t).tilted_irradiance.to_dataframe().reset_index()
Gt['G_t'] = np.round(Gt.tilted_irradiance)
if month != month_old:
print(month)
# get maximum temperature
Tmax = T_max_mmd.TmaxD.sel(month = month).to_dataframe().dropna().reset_index().drop(columns = ['lon', 'lat', 'month'])
Tmax['T_amb'] = np.round(Tmax['TmaxD'])
month_old = month
# get efficiency dataframe
eff = performance.to_dataframe().reset_index().drop(columns = ['T_cell', 'losses', 'inverter_eff', 'PF'])
# create dataset with all merged information
tmp = roofs.merge(Tmax, on = ['chx', 'chy'], how = 'left')
tmp = tmp.merge(Gt, on = 'DF_UID', how = 'left')
roofs_w_eff = tmp.merge(eff, on = ['T_amb', 'G_t'], how = 'left')
# create xarray dataset from the relevant information and save intermediate file
eff_xr = roofs_w_eff.set_index(['DF_UID', 'timestamp'])[['TmaxD', 'module_eff', 'PF_mean']].to_xarray()
eff_xr.to_netcdf( os.path.join(OUTPATH, 'tmp', 'efficiency_mapped_%d.nc' %i) )
print('Finished iteration %d in %.3fs' %(i, time.time()-tt))
## ==== MERGE INTERMEDIATE FILES ==== ##
eff = []
for i in range(10):
eff.append( xr.open_mfdataset( os.path.join(OUTPATH, 'tmp', 'efficiency_mapped_%d*.nc' %i),
chunks = {'DF_UID': 100000}) )
eff_xr = xr.concat(eff, dim = 'timestamp').sortby('timestamp')
eff_xr.to_netcdf( OUTFILE )
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