# Obtain population data for disaggregation
if config['CRITERIA']['msoa_crit']:
name_population_dataset = data['local_paths']['path_population_data_for_disaggregation_MSOA']
else:
name_population_dataset = data['local_paths']['path_population_data_for_disaggregation_lad']
data['pop_for_disag'] = data_loader.read_scenario_data(name_population_dataset)
# ---------------------------------------------
# Make selection of weather stations and data
# Load all temperature and weather station data
# ---------------------------------------------
data['weather_stations'], data['temp_data'] = data_loader.load_temp_data(
data['local_paths'],
sim_yrs=sim_yrs,
weather_realisation=weather_realisation,
path_weather_data=path_weather_data,
same_base_year_weather=False,
crit_temp_min_max=config['CRITERIA']['crit_temp_min_max'],
load_np=False,
load_parquet=False,
load_csv=True)
# Plot map with weather station
'''if config['CRITERIA']['cluster_calc'] != True:
data_loader.create_weather_station_map(
data['weather_stations'],
os.path.join(path_new_scenario, 'weatherst_distr_weathyr.pdf'),
path_shapefile=data['local_paths']['lad_shapefile'])'''
# ------------------------------------------------------------
# Disaggregate national energy demand to regional demands
# ------------------------------------------------------------
name_population_dataset = data['local_paths'][
'path_population_data_for_disaggregation_MSOA']
else:
name_population_dataset = data['local_paths'][
'path_population_data_for_disaggregation_lad']
data['pop_for_disag'] = data_loader.read_scenario_data(
name_population_dataset)
# ---------------------------------------------
# Make selection of weather stations and data
# Load all temperature and weather station data
# ---------------------------------------------
data['temp_data'] = data_loader.load_temp_data(
sim_yrs=sim_yrs,
regions=data['regions'],
weather_realisation=weather_realisation,
path_weather_data=path_weather_data,
region_name_attribute=region_name_attribute,
same_base_year_weather=False)
# ------------------------------------------------------------
# Disaggregate national energy demand to regional demands
# ------------------------------------------------------------
data['fuel_disagg'] = s_disaggregation.disaggr_demand(
data,
crit_temp_min_max=config['CRITERIA']['crit_temp_min_max'],
spatial_calibration=config['CRITERIA']['spatial_calibration'])
# ------------------------------------------------------------
# Calculate spatial diffusion factors
# ------------------------------------------------------------
def plotting_weather_data(path):
"""
Things to plot
- annual t_min of all realizations
- annual t_max of all realizations
- annual maximum t_max
- annual minimum t_min
"""
sim_yrs = range(2015, 2051, 5)
weather_reationzations = ["NF{}".format(i) for i in range(1, 101, 1)]
container_weather_stations = {}
container_temp_data = {}
# All used weather stations from model run
used_stations = [
'station_id_253', 'station_id_252', 'station_id_253', 'station_id_252',
'station_id_252', 'station_id_328', 'station_id_329', 'station_id_305',
'station_id_282', 'station_id_335', 'station_id_335', 'station_id_359',
'station_id_358', 'station_id_309', 'station_id_388', 'station_id_418',
'station_id_420', 'station_id_389', 'station_id_433', 'station_id_385',
'station_id_374', 'station_id_481', 'station_id_481', 'station_id_480',
'station_id_466', 'station_id_531', 'station_id_532', 'station_id_535',
'station_id_535', 'station_id_484', 'station_id_421', 'station_id_472',
'station_id_526', 'station_id_525', 'station_id_526', 'station_id_504',
'station_id_503', 'station_id_504', 'station_id_505', 'station_id_504',
'station_id_504', 'station_id_455', 'station_id_548', 'station_id_546',
'station_id_537', 'station_id_545', 'station_id_236', 'station_id_353',
'station_id_352', 'station_id_384', 'station_id_510', 'station_id_527',
'station_id_550', 'station_id_501', 'station_id_456', 'station_id_472',
'station_id_201', 'station_id_470', 'station_id_487', 'station_id_505',
'station_id_486', 'station_id_457', 'station_id_533', 'station_id_458',
'station_id_441', 'station_id_440', 'station_id_473', 'station_id_217',
'station_id_247', 'station_id_199', 'station_id_232', 'station_id_234',
'station_id_478', 'station_id_248', 'station_id_388', 'station_id_377',
'station_id_376', 'station_id_376', 'station_id_388', 'station_id_354',
'station_id_376', 'station_id_388', 'station_id_515', 'station_id_514',
'station_id_514', 'station_id_531', 'station_id_532', 'station_id_494',
'station_id_512', 'station_id_535', 'station_id_535', 'station_id_517',
'station_id_534', 'station_id_533', 'station_id_549', 'station_id_549',
'station_id_550', 'station_id_549', 'station_id_508', 'station_id_490',
'station_id_507', 'station_id_526', 'station_id_508', 'station_id_491',
'station_id_507', 'station_id_489', 'station_id_509', 'station_id_526',
'station_id_545', 'station_id_492', 'station_id_490', 'station_id_451',
'station_id_467', 'station_id_450', 'station_id_451', 'station_id_466',
'station_id_451', 'station_id_521', 'station_id_538', 'station_id_537',
'station_id_537', 'station_id_522', 'station_id_546', 'station_id_536',
'station_id_522', 'station_id_520', 'station_id_537', 'station_id_488',
'station_id_487', 'station_id_488', 'station_id_472', 'station_id_487',
'station_id_487', 'station_id_551', 'station_id_544', 'station_id_419',
'station_id_525', 'station_id_552', 'station_id_525', 'station_id_543',
'station_id_542', 'station_id_552', 'station_id_543', 'station_id_544',
'station_id_542', 'station_id_542', 'station_id_306', 'station_id_305',
'station_id_282', 'station_id_306', 'station_id_406', 'station_id_264',
'station_id_306', 'station_id_283', 'station_id_284', 'station_id_306',
'station_id_305', 'station_id_304', 'station_id_283', 'station_id_418',
'station_id_403', 'station_id_419', 'station_id_418', 'station_id_403',
'station_id_402', 'station_id_392', 'station_id_379', 'station_id_391',
'station_id_422', 'station_id_404', 'station_id_379', 'station_id_443',
'station_id_444', 'station_id_445', 'station_id_423', 'station_id_469',
'station_id_425', 'station_id_444', 'station_id_461', 'station_id_438',
'station_id_437', 'station_id_439', 'station_id_438', 'station_id_455',
'station_id_454', 'station_id_438', 'station_id_284', 'station_id_268',
'station_id_286', 'station_id_266', 'station_id_288', 'station_id_270',
'station_id_333', 'station_id_389', 'station_id_378', 'station_id_389',
'station_id_389', 'station_id_377', 'station_id_390', 'station_id_403',
'station_id_469', 'station_id_485', 'station_id_484', 'station_id_469',
'station_id_499', 'station_id_498', 'station_id_516', 'station_id_497',
'station_id_479', 'station_id_400', 'station_id_387', 'station_id_401',
'station_id_374', 'station_id_400', 'station_id_386', 'station_id_375',
'station_id_401', 'station_id_491', 'station_id_459', 'station_id_492',
'station_id_476', 'station_id_475', 'station_id_477', 'station_id_462',
'station_id_523', 'station_id_523', 'station_id_522', 'station_id_523',
'station_id_523', 'station_id_523', 'station_id_505', 'station_id_522',
'station_id_541', 'station_id_539', 'station_id_523', 'station_id_417',
'station_id_417', 'station_id_437', 'station_id_436', 'station_id_436',
'station_id_548', 'station_id_547', 'station_id_488', 'station_id_539',
'station_id_540', 'station_id_540', 'station_id_540', 'station_id_547',
'station_id_416', 'station_id_434', 'station_id_435', 'station_id_434',
'station_id_434', 'station_id_415', 'station_id_488', 'station_id_488',
'station_id_489', 'station_id_329', 'station_id_330', 'station_id_330',
'station_id_330', 'station_id_330', 'station_id_329', 'station_id_354',
'station_id_330', 'station_id_329', 'station_id_329', 'station_id_328',
'station_id_328', 'station_id_328', 'station_id_304', 'station_id_327',
'station_id_331', 'station_id_357', 'station_id_356', 'station_id_355',
'station_id_221', 'station_id_221', 'station_id_221', 'station_id_237',
'station_id_416', 'station_id_417', 'station_id_416', 'station_id_416',
'station_id_417', 'station_id_400', 'station_id_400', 'station_id_307',
'station_id_307', 'station_id_331', 'station_id_308', 'station_id_332',
'station_id_221', 'station_id_506', 'station_id_507', 'station_id_506',
'station_id_525', 'station_id_506', 'station_id_524', 'station_id_506',
'station_id_524', 'station_id_505', 'station_id_506', 'station_id_524',
'station_id_506', 'station_id_506', 'station_id_506', 'station_id_505',
'station_id_507', 'station_id_505', 'station_id_505', 'station_id_506',
'station_id_506', 'station_id_523', 'station_id_524', 'station_id_524',
'station_id_524', 'station_id_506', 'station_id_507', 'station_id_505',
'station_id_524', 'station_id_524', 'station_id_506', 'station_id_506',
'station_id_524', 'station_id_506', 'station_id_172', 'station_id_193',
'station_id_173', 'station_id_133', 'station_id_130', 'station_id_168',
'station_id_194', 'station_id_152', 'station_id_170', 'station_id_214',
'station_id_195', 'station_id_103', 'station_id_124', 'station_id_110',
'station_id_176', 'station_id_136', 'station_id_125', 'station_id_121',
'station_id_9', 'station_id_111', 'station_id_105', 'station_id_36',
'station_id_108', 'station_id_52', 'station_id_119', 'station_id_4',
'station_id_94', 'station_id_159', 'station_id_137', 'station_id_102',
'station_id_77', 'station_id_303', 'station_id_2', 'station_id_154',
'station_id_64', 'station_id_89', 'station_id_124', 'station_id_109',
'station_id_109', 'station_id_123', 'station_id_86', 'station_id_105',
'station_id_110', 'station_id_110', 'station_id_448', 'station_id_348',
'station_id_349', 'station_id_350', 'station_id_351', 'station_id_372',
'station_id_394', 'station_id_449', 'station_id_464', 'station_id_407',
'station_id_428', 'station_id_446', 'station_id_446', 'station_id_463',
'station_id_463', 'station_id_464', 'station_id_447', 'station_id_448',
'station_id_448', 'station_id_396', 'station_id_447'
]
# Load full data
for weather_realisation in weather_reationzations:
print("weather_realisation: " + str(weather_realisation))
path_weather_data = path
weather_stations, temp_data = data_loader.load_temp_data(
{},
sim_yrs=sim_yrs,
weather_realisation=weather_realisation,
path_weather_data=path_weather_data,
same_base_year_weather=False,
crit_temp_min_max=True,
load_np=False,
load_parquet=False,
load_csv=True)
# Load only data from selected weather stations
temp_data_used = {}
for year in sim_yrs:
temp_data_used[year] = {}
all_station_data = temp_data[year].keys()
for station in all_station_data:
if station in used_stations:
temp_data_used[year][station] = temp_data[year][station]
container_weather_stations[weather_realisation] = weather_stations
container_temp_data[weather_realisation] = temp_data_used
# Create plot with daily min
print("... creating min max plot")
t_min_average_every_day = []
t_max_average_every_day = []
t_min_min_every_day = []
t_max_max_every_day = []
std_dev_t_min = []
std_dev_t_max = []
std_dev_t_min_min = []
std_dev_t_max_max = []
for year in sim_yrs:
for realization in container_weather_stations.keys():
t_min_average_stations = []
t_max_average_stations = []
t_min_min_average_stations = []
t_max_max_average_stations = []
stations_data = container_temp_data[realization][year]
for station in stations_data.keys():
t_min_annual_average = np.average(
stations_data[station]['t_min'])
t_max_annual_average = np.average(
stations_data[station]['t_max'])
t_min_min_stations = np.min(stations_data[station]['t_min'])
t_max_max_stations = np.max(stations_data[station]['t_max'])
t_min_average_stations.append(
t_min_annual_average) #average cross all stations
t_max_average_stations.append(
t_max_annual_average) #average cross all stations
t_min_min_average_stations.append(t_min_min_stations)
t_max_max_average_stations.append(t_max_max_stations)
av_t_min = np.average(
t_min_average_stations) #average across all realizations
av_t_max = np.average(
t_max_average_stations) #average across all realizations
av_min_t_min = np.average(
t_min_min_average_stations) #average across all realizations
av_max_t_max = np.average(
t_max_max_average_stations) #average across all realizations
std_t_min = np.std(t_min_average_stations)
std_t_max = np.std(t_max_average_stations)
std_t_min_min = np.std(t_min_min_average_stations)
std_t_max_max = np.std(t_max_max_average_stations)
t_min_average_every_day.append(av_t_min)
t_max_average_every_day.append(av_t_max)
t_min_min_every_day.append(av_min_t_min)
t_max_max_every_day.append(av_max_t_max)
std_dev_t_min.append(std_t_min)
std_dev_t_max.append(std_t_max)
std_dev_t_min_min.append(std_t_min_min)
std_dev_t_max_max.append(std_t_max_max)
# Plot variability
fig = plt.figure(figsize=basic_plot_functions.cm2inch(9,
6)) #width, height
colors = {
't_min': 'steelblue',
't_max': 'tomato',
't_min_min': 'peru',
't_max_max': 'r'
}
# plot
plt.plot(sim_yrs,
t_min_average_every_day,
color=colors['t_min'],
label="t_min")
plt.plot(sim_yrs,
t_max_average_every_day,
color=colors['t_max'],
label="t_max")
plt.plot(sim_yrs,
t_min_min_every_day,
color=colors['t_min_min'],
label="t_min_min")
plt.plot(sim_yrs,
t_max_max_every_day,
color=colors['t_max_max'],
label="t_max_max")
# Variations
plt.fill_between(
sim_yrs,
list(
np.array(t_min_average_every_day) - (2 * np.array(std_dev_t_min))),
list(
np.array(t_min_average_every_day) + (2 * np.array(std_dev_t_min))),
color=colors['t_min'],
alpha=0.25)
plt.fill_between(
sim_yrs,
list(
np.array(t_max_average_every_day) - (2 * np.array(std_dev_t_max))),
list(
np.array(t_max_average_every_day) + (2 * np.array(std_dev_t_max))),
color=colors['t_max'],
alpha=0.25)
plt.fill_between(
sim_yrs,
list(
np.array(t_min_min_every_day) - (2 * np.array(std_dev_t_min_min))),
list(
np.array(t_min_min_every_day) + (2 * np.array(std_dev_t_min_min))),
color=colors['t_min_min'],
alpha=0.25)
plt.fill_between(
sim_yrs,
list(
np.array(t_max_max_every_day) - (2 * np.array(std_dev_t_max_max))),
list(
np.array(t_max_max_every_day) + (2 * np.array(std_dev_t_max_max))),
color=colors['t_max_max'],
alpha=0.25)
# Legend
legend = plt.legend(ncol=2,
prop={'size': 10},
loc='upper center',
bbox_to_anchor=(0.5, -0.1),
frameon=False)
legend.get_title().set_fontsize(8)
result_path = "C:/_scrap/"
seperate_legend = True
if seperate_legend:
basic_plot_functions.export_legend(
legend,
os.path.join(result_path, "{}__legend.pdf".format(result_path)))
legend.remove()
plt.legend(ncol=2)
plt.xlabel("Year")
plt.ylabel("Temperature (°C)")
plt.tight_layout()
plt.margins(x=0)
fig.savefig(os.path.join(result_path, "test.pdf"))
data['assumptions'].update('strategy_variables', strategy_variables)
data['tech_lp'] = data_loader.load_data_profiles(
data['paths'], data['local_paths'], data['assumptions'].model_yeardays,
data['assumptions'].model_yeardays_daytype,
data['criterias']['plot_tech_lp'])
technologies = non_param_assumptions.update_technology_assumption(
data['assumptions'].technologies,
data['assumptions'].strategy_variables['f_eff_achieved']
['scenario_value'],
data['assumptions'].strategy_variables['gshp_fraction_ey']
['scenario_value'])
data['technologies'] = technologies
data['weather_stations'], data['temp_data'] = data_loader.load_temp_data(
data['local_paths'])
data['reg_nrs'] = len(data['regions'])
# ------------------------------
if data['criterias']['virtual_building_stock_criteria']:
rs_floorarea, ss_floorarea = data_loader.floor_area_virtual_dw(
data['regions'],
data['sectors']['all_sectors'],
data['local_paths'],
data['assumptions'].base_yr,
f_mixed_floorarea=data['assumptions'].f_mixed_floorarea)
# Lookup table to import industry sectoral gva
lookup_tables.industrydemand_name_sic2007()