def calc_av_per_season_fueltype(results_every_year, seasons, model_yeardays_daytype):
"""Calculate average demand per season and fueltype for every fueltype
Arguments
---------
results_every_year : dict
Results for every year
seasons : dict
Seasons
model_yeardays_daytype : list
Daytype of modelled days
Returns
-------
av_season_daytype_cy :
Average demand per season and daytype
season_daytype_cy :
Demand per season and daytpe
"""
av_season_daytype_cy = defaultdict(dict)
season_daytype_cy = defaultdict(dict)
for year, fueltypes_data in results_every_year.items():
for fueltype, reg_fuels in enumerate(fueltypes_data):
# Summarise across regions
tot_all_reg_fueltype = np.sum(reg_fuels, axis=0)
tot_all_reg_fueltype_reshape = tot_all_reg_fueltype.reshape((365, 24))
calc_av, calc_lp = load_profile.calc_av_lp(
tot_all_reg_fueltype_reshape,
seasons,
model_yeardays_daytype)
av_season_daytype_cy[year][fueltype] = calc_av
season_daytype_cy[year][fueltype] = calc_lp
return dict(av_season_daytype_cy), dict(season_daytype_cy)
def spatio_temporal_val(ed_fueltype_national_yh, ed_fueltype_regs_yh,
result_paths, paths, regions, seasons,
model_yeardays_daytype, plot_crit):
"""Validate spatial and temporal energy demands
Info
-----
Because the floor area is only availabe for LADs from 2001,
the LADs are converted to 2015 LADs.
"""
logging.info("... temporal validation")
fueltypes = lookup_tables.basic_lookups()['fueltypes']
# -------------------------------------------
# Spatial validation after calculations
# -------------------------------------------
subnational_elec = data_loader.read_lad_demands(
paths['val_subnational_elec'])
subnational_gas = data_loader.read_lad_demands(
paths['val_subnational_gas'])
# Create fueltype secific dict
fuel_elec_regs_yh = {}
for region_array_nr, region in enumerate(regions):
fuel_elec_regs_yh[region] = np.sum(
ed_fueltype_regs_yh[fueltypes['electricity']][region_array_nr])
# Create fueltype secific dict
fuel_gas_regs_yh = {}
for region_array_nr, region in enumerate(regions):
fuel_gas_regs_yh[region] = np.sum(
ed_fueltype_regs_yh[fueltypes['gas']][region_array_nr])
# ----------------------------------------
# Remap demands between 2011 and 2015 LADs
# ----------------------------------------
subnational_elec = map_LAD_2011_2015(subnational_elec)
subnational_gas = map_LAD_2011_2015(subnational_gas)
fuel_elec_regs_yh = map_LAD_2011_2015(fuel_elec_regs_yh)
fuel_gas_regs_yh = map_LAD_2011_2015(fuel_gas_regs_yh)
spatial_validation(fuel_elec_regs_yh,
subnational_elec,
regions,
'elec',
fig_name=os.path.join(
result_paths['data_results_validation'],
'validation_spatial_elec_post_calcualtion.pdf'),
label_points=False,
plotshow=plot_crit)
spatial_validation(fuel_gas_regs_yh,
subnational_gas,
regions,
'gas',
fig_name=os.path.join(
result_paths['data_results_validation'],
'validation_spatial_gas_post_calcualtion.pdf'),
label_points=False,
plotshow=plot_crit)
# -------------------------------------------
# Temporal validation (hourly for national)
# -------------------------------------------
# Read validation data
elec_2015_indo, elec_2015_itsdo = elec_national_data.read_raw_elec_2015(
paths['val_nat_elec_data'])
f_diff_elec = np.sum(ed_fueltype_national_yh[
fueltypes['electricity']]) / np.sum(elec_2015_indo)
logging.info("... ed diff modellend and real [p] %s: ",
(1 - f_diff_elec) * 100)
elec_factored_yh = f_diff_elec * elec_2015_indo
temporal_validation(result_paths,
ed_fueltype_national_yh[fueltypes['electricity']],
elec_factored_yh, plot_crit)
# ---------------------------------------------------
# Calculate average season and daytypes and plot
# ---------------------------------------------------
logging.info("...calculate average data and plot per season and fueltype")
calc_av_lp_modelled, calc_lp_modelled = load_profile.calc_av_lp(
ed_fueltype_national_yh[fueltypes['electricity']], seasons,
model_yeardays_daytype)
calc_av_lp_real, calc_lp_real = load_profile.calc_av_lp(
elec_factored_yh, seasons, model_yeardays_daytype)
# Plot average daily loads
fig_load_profile_dh_multiple.run(
path_fig_folder=result_paths['data_results_validation'],
path_plot_fig=os.path.join(result_paths['data_results_validation'],
'validation_all_season_daytypes.pdf'),
calc_av_lp_modelled=calc_av_lp_modelled,
calc_av_lp_real=calc_av_lp_real,
calc_lp_modelled=calc_lp_modelled,
calc_lp_real=calc_lp_real,
plot_peak=True,
plot_radar=False,
plot_all_entries=False,
plot_max_min_polygon=True,
plotshow=False,
max_y_to_plot=60,
fueltype_str=False,
year=False)
# ---------------------------------------------------
# Validation of national electrictiy demand for peak
# ---------------------------------------------------
logging.debug("...validation of peak data: compare peak with data")
# Because the coldest day is not the same for every region,
# the coldest day needs to be defined manually or defined
# by getting the hours with maximum electricity demand
# Peak across all fueltypes WARNING: Fueltype specific
peak_day_all_fueltypes = enduse_func.get_peak_day_all_fueltypes(
ed_fueltype_national_yh)
logging.info("Peak day 'peak_day_all_fueltypes': " +
str(peak_day_all_fueltypes))
fueltype = fueltypes['electricity']
peak_day_electricity, _ = enduse_func.get_peak_day_single_fueltype(
ed_fueltype_national_yh[fueltype])
logging.info("Peak day 'peak_day_electricity': " +
str(peak_day_electricity))
elec_national_data.compare_peak(
"validation_peak_elec_day_all_fueltypes.pdf",
result_paths['data_results_validation'],
elec_2015_indo[peak_day_all_fueltypes], ed_fueltype_national_yh[
fueltypes['electricity']][peak_day_all_fueltypes],
peak_day_all_fueltypes)
elec_national_data.compare_peak(
"validation_peak_elec_day_only_electricity.pdf",
result_paths['data_results_validation'],
elec_2015_indo[peak_day_electricity], ed_fueltype_national_yh[
fueltypes['electricity']][peak_day_electricity],
peak_day_electricity)
# Manual peak day
peak_day = 19
elec_national_data.compare_peak(
"validation_elec_peak_day_{}.pdf".format(peak_day),
result_paths['data_results_validation'], elec_factored_yh[peak_day],
ed_fueltype_national_yh[fueltypes['electricity']][peak_day], peak_day)
peak_day = 33
elec_national_data.compare_peak(
"validation_elec_peak_day_{}.pdf".format(peak_day),
result_paths['data_results_validation'], elec_factored_yh[peak_day],
ed_fueltype_national_yh[fueltypes['electricity']][peak_day], peak_day)
peak_day_real_electricity, _ = enduse_func.get_peak_day_single_fueltype(
elec_2015_indo)
logging.info("Peak day 'peak_day_electricity': " +
str(peak_day_real_electricity))
#raise Exception
elec_national_data.compare_peak(
"validation_elec_peak_day_{}.pdf".format(peak_day_real_electricity),
result_paths['data_results_validation'], elec_factored_yh[peak_day],
ed_fueltype_national_yh[fueltypes['electricity']]
[peak_day_real_electricity], peak_day_real_electricity)
# ---------------------------------------------------
# Validate boxplots for every hour (temporal validation)
# ---------------------------------------------------
elec_national_data.compare_results_hour_boxplots(
"validation_hourly_boxplots_electricity_01.pdf",
result_paths['data_results_validation'], elec_2015_indo,
ed_fueltype_national_yh[fueltypes['electricity']])
return
def tempo_spatial_validation(base_yr, model_yearhours_nrs, model_yeardays_nrs,
scenario_data, ed_fueltype_national_yh,
ed_fueltype_regs_yh, fueltypes, fueltypes_nr,
result_paths, paths, regions, reg_coord, seasons,
model_yeardays_daytype, plot_crit):
"""Validate national hourly demand for yearls fuel
for all LADs. Test how the national disaggregation
works.
Info
-----
Because the floor area is only availabe for LADs from 2001,
the LADs are converted to 2015 LADs.
"""
logging.info("... spatial validation")
# -------------------------------------------
# Add electricity and gas for transportation sector
# -------------------------------------------
fueltype_elec = fueltypes['electricity']
fuel_ktoe_transport_2015 = 385
fuel_national_tranport = np.zeros((fueltypes_nr), dtype=float)
# Elec demand from ECUK for transport sector
fuel_national_tranport[fueltype_elec] = conversions.ktoe_to_gwh(
fuel_ktoe_transport_2015)
# Create transport model (add flat shapes)
model_object_transport = generic_shapes.GenericFlatEnduse(
fuel_national_tranport, model_yeardays_nrs)
# Add national fuel
ed_fueltype_national_yh = np.add(ed_fueltype_national_yh,
model_object_transport.fuel_yh)
# Add electricity of transportion to regional yh fuel proportionally to population
for region_array_nr, region in enumerate(regions):
# Disaggregation factor for transport electricity
factor_transport_reg = scenario_data['population'][base_yr][
region] / sum(scenario_data['population'][base_yr].values())
ed_fueltype_regs_yh[fueltype_elec][
region_array_nr] += model_object_transport.fuel_yh[
fueltype_elec].reshape(
model_yearhours_nrs) * factor_transport_reg
# -------------------------------------------
# Spatial validation
# -------------------------------------------
subnational_elec = data_loader.read_national_real_elec_data(
paths['path_val_subnational_elec'])
subnational_gas = data_loader.read_national_real_gas_data(
paths['path_val_subnational_gas'])
# Create fueltype secific dict
fuel_elec_regs_yh = {}
for region_array_nr, region in enumerate(regions):
gwh_modelled = np.sum(
ed_fueltype_regs_yh[fueltypes['electricity']][region_array_nr])
fuel_elec_regs_yh[region] = gwh_modelled
# Create fueltype secific dict
fuel_gas_regs_yh = {}
for region_array_nr, region in enumerate(regions):
gwh_modelled = np.sum(
ed_fueltype_regs_yh[fueltypes['gas']][region_array_nr])
fuel_gas_regs_yh[region] = gwh_modelled
# ----------------------------------------
# Remap demands between 2011 and 2015 LADs
# ----------------------------------------
subnational_elec = map_LAD_2011_2015(subnational_elec)
subnational_gas = map_LAD_2011_2015(subnational_gas)
fuel_elec_regs_yh = map_LAD_2011_2015(fuel_elec_regs_yh)
fuel_gas_regs_yh = map_LAD_2011_2015(fuel_gas_regs_yh)
logging.info("Validation of electricity")
spatial_validation(reg_coord,
fuel_elec_regs_yh,
subnational_elec,
regions,
'elec',
os.path.join(result_paths['data_results_validation'],
'validation_spatial_elec.pdf'),
label_points=False,
plotshow=plot_crit)
logging.info("Validation of gas")
spatial_validation(reg_coord,
fuel_gas_regs_yh,
subnational_gas,
regions,
'gas',
os.path.join(result_paths['data_results_validation'],
'validation_spatial_gas.pdf'),
label_points=False,
plotshow=plot_crit)
# -------------------------------------------
# Temporal validation (hourly for national)
# -------------------------------------------
# Read validation data
elec_2015_indo, elec_2015_itsdo = elec_national_data.read_raw_elec_2015(
paths['path_val_nat_elec_data'])
f_diff_elec = np.sum(ed_fueltype_national_yh[
fueltypes['electricity']]) / np.sum(elec_2015_indo)
logging.info("... ed diff modellend and real [p] %s: ",
(1 - f_diff_elec) * 100)
elec_factored_yh = f_diff_elec * elec_2015_indo
temporal_validation(result_paths,
ed_fueltype_national_yh[fueltypes['electricity']],
elec_factored_yh, elec_2015_indo, elec_2015_itsdo,
plot_crit)
# ---------------------------------------------------
# Calculate average season and daytypes and plot
# ---------------------------------------------------
logging.info("...calculate average data and plot per season and fueltype")
calc_av_lp_modelled, calc_lp_modelled = load_profile.calc_av_lp(
ed_fueltype_national_yh[fueltypes['electricity']], seasons,
model_yeardays_daytype)
calc_av_lp_real, calc_lp_real = load_profile.calc_av_lp(
elec_factored_yh, seasons, model_yeardays_daytype)
# Plot average daily loads
plotting_results.plot_load_profile_dh_multiple(
path_fig_folder=result_paths['data_results_validation'],
path_plot_fig=os.path.join(result_paths['data_results_validation'],
'validation_all_season_daytypes.pdf'),
calc_av_lp_modelled=calc_av_lp_modelled,
calc_av_lp_real=calc_av_lp_real,
calc_lp_modelled=calc_lp_modelled,
calc_lp_real=calc_lp_real,
plot_peak=True,
plot_radar=False,
plot_all_entries=False,
plot_max_min_polygon=True,
plotshow=False,
max_y_to_plot=60,
fueltype_str=False,
year=False)
# ---------------------------------------------------
# Validation of national electrictiy demand for peak
# ---------------------------------------------------
logging.debug("...validation of peak data: compare peak with data")
# Peak across all fueltypes WARNING: Fueltype specific
peak_day = enduse_func.get_peak_day_all_fueltypes(ed_fueltype_national_yh)
elec_national_data.compare_peak(
"validation_elec_peak_comparison_peakday_yh.pdf",
result_paths['data_results_validation'], elec_2015_indo[peak_day],
ed_fueltype_national_yh[fueltypes['electricity']][peak_day])
# ---------------------------------------------------
# Validate boxplots for every hour (temporal validation)
# ---------------------------------------------------
elec_national_data.compare_results_hour_boxplots(
"validation_hourly_boxplots_electricity_01.pdf",
result_paths['data_results_validation'], elec_2015_indo,
ed_fueltype_national_yh[fueltypes['electricity']])
return
def test_calc_av_lp():
model_yeardays_daytype = [
'holiday',
'workday',
'workday',
'workday',
'holiday',
'workday',
'workday',
'workday',
'holiday',
'workday',
'workday',
'workday',
'holiday',
'workday',
'workday',
'workday',
] #
# Example with 16 days
seasons = {}
seasons['winter'] = [0, 1, 2, 3]
seasons['spring'] = [4, 5, 6, 7]
seasons['summer'] = [8, 9, 10, 11]
seasons['autumn'] = [12, 13, 14, 15]
demand_yh = np.zeros((16, 24)) + 10
for i in range(4):
demand_yh[i] = 12
for i in range(4, 8):
demand_yh[i] = 10
# weekend
demand_yh[0] = 100
demand_yh[4] = 200
result_av, _ = load_profile.calc_av_lp(demand_yh, seasons,
model_yeardays_daytype)
expected = {
'winter': {
'workday': np.full((24), 12),
'holiday': np.full((24), 100)
},
'spring': {
'workday': np.full((24), 10),
'holiday': np.full((24), 200)
},
'summer': {
'workday': np.full((24), 12),
'holiday': np.full((24), 100)
},
'autmn': {
'workday': np.full((24), 10),
'holiday': np.full((24), 10)
}
}
np.testing.assert_array_equal(expected['winter']['workday'],
result_av['winter']['workday'])
np.testing.assert_array_equal(expected['winter']['holiday'],
result_av['winter']['holiday'])
np.testing.assert_array_equal(expected['spring']['workday'],
result_av['spring']['workday'])
np.testing.assert_array_equal(expected['spring']['holiday'],
result_av['spring']['holiday'])