add the possibility to calibrate spatial disaggregation on LAD level …

…with electricity demand data

energy_demand/config_data/05-technologies/technology_definition.csv

@@ -1,48 +1,48 @@
 technology,fueltype,efficiency in base year,efficiency in future year,year when efficiency is fully realised,diffusion method (sigmoid or linear),market_entry,technology type,maximum theoretical service share of technology,description
 standard_lighting_bulb,electricity,0.029,0.034,2050,linear,2000,lighting,1,
 halogen,electricity,0.037,0.073,2050,linear,2000,lighting,1,
-energy_saving_lighting_bulb,electricity,0.220,0.293,2050,linear,2000,lighting,1,
-fluorescent_strip_lighting,electricity,0.146,0.220,2050,linear,2000,lighting,1,
-LED,electricity,0.220,0.439,2050,linear,2000,lighting,1,
-washing_machine,electricity,1.000,1.334,2050,linear,2000,wet,1,
-tumble_dryer,electricity,1.000,3.076,2050,linear,2000,wet,1,
-dishwasher,electricity,1.000,1.367,2050,linear,2000,wet,1,
-washer_dryer,electricity,1.000,1.334,2050,linear,2000,wet,1,
-hob_gas,gas,0.400,0.500,2050,linear,2000,cooking,1,
-hob_electricity,electricity,0.600,0.700,2050,linear,2000,cooking,1,
-hob_induction_electricity,electricity,0.750,0.900,2050,linear,2000,cooking,1,
-hob_hydrogen,hydrogen,0.400,0.500,2050,linear,2000,cooking,1,
-hob_biomass,biomass,0.400,0.500,2050,linear,2000,cooking,1,
-chest_freezer,electricity,1.000,2.709,2050,linear,2000,cold,1,
-fridge_freezer,electricity,1.000,2.405,2050,linear,2000,cold,1,
-refrigerator,electricity,1.000,2.408,2050,linear,2000,cold,1,
-upright_freezer,electricity,1.000,2.500,2050,linear,2000,cold,1,
-central_air_conditioner_electricity,electricity,1.000,2.000,2050,linear,2000,cooling_const,1,
-decentral_air_conditioner_electricity,electricity,1.000,2.000,2050,linear,2000,cooling_const,1,
-central_air_conditioner_oil,oil,1.000,2.000,2050,linear,2000,cooling_const,1,
-decentral_air_conditioner_oil,oil,1.000,2.000,2050,linear,2000,cooling_const,1,
-central_air_conditioner_gas,gas,1.000,2.000,2050,linear,2000,cooling_const,0.89,
-decentral_air_conditioner_gas,gas,1.000,2.000,2050,linear,2000,cooling_const,0.89,
-boiler_solid_fuel,solid_fuel,0.600,0.700,2050,linear,2000,heating_const,1,
-boiler_gas,gas,0.650,0.940,2050,linear,2000,heating_const,0.89,
-boiler_electricity,electricity,0.990,0.990,2050,linear,2000,heating_const,1,
-boiler_oil,oil,0.700,0.940,2050,linear,2000,heating_const,1,
-boiler_biomass,biomass,0.600,0.950,2050,linear,2000,heating_const,1,
-boiler_hydrogen,hydrogen,0.850,0.950,2050,linear,2000,heating_const,0.89,
-boiler_condensing_gas,gas,0.850,0.950,2050,linear,2000,heating_const,1,
-boiler_condensing_oil,oil,0.850,0.950,2050,linear,2000,heating_const,1,
-stirling_micro_CHP_gas,gas,0.800,0.950,2050,linear,2000,tech_CHP,0.89,
-fuel_cell_hydrogen,hydrogen,0.850,0.950,2050,linear,2000,tech_CHP,0.89,
-storage_heater_electricity,electricity,0.990,0.990,2050,linear,2000,storage_heating_electricity,1,
-secondary_heater_electricity,electricity,0.990,0.990,2050,linear,2000,secondary_heating_electricity,1,
-heat_pump_ASHP_electricity,electricity,3.600,4.000,2050,linear,2000,heating_non_const,1,
-heat_pump_GSHP_electricity,electricity,4.900,5.400,2050,linear,2000,heating_non_const,1,
-heat_pump_ASHP_hydrogen,hydrogen,3.600,4.000,2050,linear,2000,heating_non_const,0.89,
-heat_pump_GSHP_hydrogen,hydrogen,4.900,5.400,2050,linear,2000,heating_non_const,0.89,
-district_heating_CHP_gas,gas,0.450,0.450,2050,linear,2000,tech_CHP,0.2,thermal efficiency
-district_heating_electricity,electricity,1.000,1.000,2050,linear,2000,tech_district_heating,0.2,
-district_heating_biomass,biomass,0.600,0.600,2050,linear,2000,tech_district_heating,0.2,
-district_heating_fuel_cell,hydrogen,0.850,0.850,2050,linear,2000,tech_district_heating,0.2,
+energy_saving_lighting_bulb,electricity,0.22,0.293,2050,linear,2000,lighting,1,
+fluorescent_strip_lighting,electricity,0.146,0.22,2050,linear,2000,lighting,1,
+LED,electricity,0.22,0.439,2050,linear,2000,lighting,1,
+washing_machine,electricity,1,1.334,2050,linear,2000,wet,1,
+tumble_dryer,electricity,1,3.076,2050,linear,2000,wet,1,
+dishwasher,electricity,1,1.367,2050,linear,2000,wet,1,
+washer_dryer,electricity,1,1.334,2050,linear,2000,wet,1,
+hob_gas,gas,0.4,0.5,2050,linear,2000,cooking,1,
+hob_electricity,electricity,0.6,0.7,2050,linear,2000,cooking,1,
+hob_induction_electricity,electricity,0.75,0.9,2050,linear,2000,cooking,1,
+hob_hydrogen,hydrogen,0.4,0.5,2050,linear,2000,cooking,1,
+hob_biomass,biomass,0.4,0.5,2050,linear,2000,cooking,1,
+chest_freezer,electricity,1,2.709,2050,linear,2000,cold,1,
+fridge_freezer,electricity,1,2.405,2050,linear,2000,cold,1,
+refrigerator,electricity,1,2.408,2050,linear,2000,cold,1,
+upright_freezer,electricity,1,2.5,2050,linear,2000,cold,1,
+central_air_conditioner_electricity,electricity,1,2,2050,linear,2000,cooling_const,1,
+decentral_air_conditioner_electricity,electricity,1,2,2050,linear,2000,cooling_const,1,
+central_air_conditioner_oil,oil,1,2,2050,linear,2000,cooling_const,1,
+decentral_air_conditioner_oil,oil,1,2,2050,linear,2000,cooling_const,1,
+central_air_conditioner_gas,gas,1,2,2050,linear,2000,cooling_const,0.89,
+decentral_air_conditioner_gas,gas,1,2,2050,linear,2000,cooling_const,0.89,
+boiler_solid_fuel,solid_fuel,0.6,0.7,2050,linear,2000,heating_const,1,
+boiler_gas,gas,0.65,0.94,2050,linear,2000,heating_const,0.89,
+boiler_electricity,electricity,0.99,0.99,2050,linear,2000,heating_const,1,
+boiler_oil,oil,0.7,0.94,2050,linear,2000,heating_const,1,
+boiler_biomass,biomass,0.6,0.95,2050,linear,2000,heating_const,1,
+boiler_hydrogen,hydrogen,0.85,0.95,2050,linear,2000,heating_const,0.89,
+boiler_condensing_gas,gas,0.85,0.95,2050,linear,2000,heating_const,1,
+boiler_condensing_oil,oil,0.85,0.95,2050,linear,2000,heating_const,1,
+stirling_micro_CHP_gas,gas,0.8,0.95,2050,linear,2000,tech_CHP,0.89,
+fuel_cell_hydrogen,hydrogen,0.85,0.95,2050,linear,2000,tech_CHP,0.89,
+storage_heater_electricity,electricity,0.99,0.99,2050,linear,2000,storage_heating_electricity,1,
+secondary_heater_electricity,electricity,0.99,0.99,2050,linear,2000,secondary_heating_electricity,1,
+heat_pump_ASHP_electricity,electricity,3.6,4,2050,linear,2000,heating_non_const,1,
+heat_pump_GSHP_electricity,electricity,4.9,5.4,2050,linear,2000,heating_non_const,1,
+heat_pump_ASHP_hydrogen,hydrogen,3.6,4,2050,linear,2000,heating_non_const,0.89,
+heat_pump_GSHP_hydrogen,hydrogen,4.9,5.4,2050,linear,2000,heating_non_const,0.89,
+district_heating_CHP_gas,gas,0.45,0.45,2050,linear,2000,tech_CHP,0.2,thermal efficiency
+district_heating_electricity,electricity,1,1,2050,linear,2000,tech_district_heating,0.2,
+district_heating_biomass,biomass,0.6,0.6,2050,linear,2000,tech_district_heating,0.2,
+district_heating_fuel_cell,hydrogen,0.85,0.85,2050,linear,2000,tech_district_heating,0.2,
 basic_oxygen_furnace,solid_fuel,1,1,2050,linear,2010,steel_production,1,assumed no technology imporvement
 electric_arc_furnace,electricity,6.1,6.1,2050,linear,2010,steel_production,1,assumed no technology imporvement
 SNG_furnace,gas,1,1,2050,linear,2020,steel_production,1,assumed no technology imporvement

energy_demand/main.py

@@ -119,6 +119,7 @@ def energy_demand_model(
     data['criterias'] = {}
     data['criterias']['mode_constrained'] = True                    # True: Technologies are defined in ED model and fuel is provided, False: Heat is delievered not per technologies
     data['criterias']['virtual_building_stock_criteria'] = True     # True: Run virtual building stock model
+    data['criterias']['spatial_calibration'] = False
 
     fast_model_run = False
     if fast_model_run == True:
@@ -326,7 +327,8 @@ def energy_demand_model(
     # ------------------------------------------------------------
     # Disaggregate national energy demand to regional demands
     # ------------------------------------------------------------
-    data['fuel_disagg'] = s_disaggregation.disaggr_demand(data)
+    data['fuel_disagg'] = s_disaggregation.disaggr_demand(
+        data, spatial_calibration=data['criterias']['spatial_calibration'])
 
     # ------------------------------------------------------------
     # Calculate spatial diffusion factors

energy_demand/plotting/_scrap.py

@@ -1,5 +1,64 @@
 """Fig 2 figure
 """
+
+import numpy as np
+
+
+a = np.ones((3,3))
+
+calc_lf_y(a)
+
+def calc_lf_y(fuel_yh):
+    """Calculate the yearly load factor for every fueltype
+    by dividing the yearly average load by the peak hourly
+    load in a year.
+
+    Arguments
+    ---------
+    fuel_yh : array (fueltypes, 365, 24) or (fueltypes, 8760)
+        Yh fuel
+
+    Returns
+    -------
+    load_factor_y : array
+        Yearly load factors as percentage (100% = 100)
+
+    Note
+    -----
+        LECTRICAL AND PRODUCTION LOAD FACTORS
+        ## WRONG Load factor = average load / maximum load in given time period
+        Load factor = average load / peak load    # * meausred hours
+        https://en.wikipedia.org/wiki/Load_factor_(electrical)
+
+        https://circuitglobe.com/load-factor.html
+    """
+    if fuel_yh.shape[1] == 365:
+        fuel_yh_8760 = fuel_yh.reshape(fuel_yh.shape[0], 8760)
+    else:
+        fuel_yh_8760 = fuel_yh
+
+    # Get total sum per fueltype
+    tot_load_y = np.sum(fuel_yh_8760, axis=1)
+
+    # Calculate maximum hour in every day of a year
+    max_load_h = np.max(fuel_yh_8760, axis=1)
+
+    # Caclualte yearly load factor for every fueltype
+    with np.errstate(divide='ignore', invalid='ignore'):
+        load_factor_y = tot_load_y / (max_load_h * 8760)
+    load_factor_y[np.isnan(load_factor_y)] = 0
+
+    return load_factor_y * 100
+
+
+
+
+
+
+
+
+
+
 import numpy as np
 import matplotlib.pyplot as plt
 #from scipy.stats import mstats

energy_demand/profiles/load_factors.py

@@ -121,12 +121,9 @@ def calc_lf_y(fuel_yh):
 
     Note
     -----
-        LECTRICAL AND PRODUCTION LOAD FACTORS
-        ## WRONG Load factor = average load / maximum load in given time period
-        Load factor = energy consumption (kWH) / peak demand (kW) * meausred hours
-        https://en.wikipedia.org/wiki/Load_factor_(electrical)
-
-        https://circuitglobe.com/load-factor.html
+    Load factor = average load / peak load
+    https://en.wikipedia.org/wiki/Load_factor_(electrical)
+    https://circuitglobe.com/load-factor.html
     """
     if fuel_yh.shape[1] == 365:
         fuel_yh_8760 = fuel_yh.reshape(fuel_yh.shape[0], 8760)
@@ -141,7 +138,8 @@ def calc_lf_y(fuel_yh):
 
     # Caclualte yearly load factor for every fueltype
     with np.errstate(divide='ignore', invalid='ignore'):
-        load_factor_y = tot_load_y / (max_load_h * 8760)
+        load_factor_y = (tot_load_y / 8760) / max_load_h
+
     load_factor_y[np.isnan(load_factor_y)] = 0
 
     return load_factor_y * 100

energy_demand/read_write/data_loader.py

@@ -306,7 +306,11 @@ def read_national_real_elec_data(path_to_csv):
         for row in rows:
             geocode = str.strip(row[read_data.get_position(headings, 'LA Code')])
             tot_consumption_unclean = row[read_data.get_position(headings, 'Total consumption')].strip()
-            national_fuel_data[geocode] = float(tot_consumption_unclean.replace(",", ""))
+            try:
+                national_fuel_data[geocode] = float(tot_consumption_unclean.replace(",", ""))
+            except:
+                # no data provided
+                logging.debug("No validation data available for region %s", geocode)
 
     return national_fuel_data
 
@@ -518,19 +522,15 @@ def get_local_paths(path):
         'lad_shapefile': os.path.join(
             path, '_raw_data', 'C_LAD_geography', 'same_as_pop_scenario', 'lad_2016_uk_simplified.shp'),
         'path_post_installation_data': os.path.join(
-            path, '_processed_data', '_post_installation_data'),
-        'data_processed_disaggregated': os.path.join(
-            path, '_processed_data', '_post_installation_data', 'disaggregated'),
+            path, '_processed_data'),
         'weather_data': os.path.join(
             path, '_raw_data', 'A-temperature_data', 'cleaned_weather_stations_data'),
         'load_profiles': os.path.join(
-            path, '_processed_data', '_post_installation_data', 'load_profiles'),
-        'dir_disaggregated': os.path.join(
-            path, '_processed_data', '_post_installation_data', 'disaggregated'),
+            path, '_processed_data', 'load_profiles'),
         'rs_load_profile_txt': os.path.join(
-            path, '_processed_data', '_post_installation_data', 'load_profiles', 'rs_submodel'),
+            path, '_processed_data', 'load_profiles', 'rs_submodel'),
         'ss_load_profile_txt': os.path.join(
-            path, '_processed_data', '_post_installation_data', 'load_profiles', 'ss_submodel'),
+            path, '_processed_data', 'load_profiles', 'ss_submodel'),
         'yaml_parameters': os.path.join(
             path, '..', 'config', 'yaml_parameters.yml'),
         'yaml_parameters_constrained': os.path.join(
@@ -630,23 +630,23 @@ def load_paths(path):
             path, '04-census_data', 'LAD_census_data.csv'),
 
         # Validation datasets
-        'path_val_subnational_elec': os.path.join(
+        'val_subnational_elec': os.path.join(
             path, '01-validation_datasets', '02_subnational_elec', 'data_2015_elec.csv'),
-        'path_val_subnational_elec_residential': os.path.join(
+        'val_subnational_elec_residential': os.path.join(
             path, '01-validation_datasets', '02_subnational_elec', 'data_2015_elec_domestic.csv'),
-        'path_val_subnational_elec_non_residential': os.path.join(
+        'val_subnational_elec_non_residential': os.path.join(
             path, '01-validation_datasets', '02_subnational_elec', 'data_2015_elec_non_domestic.csv'),
-        'path_val_subnational_elec_msoa_residential': os.path.join(
+        'val_subnational_elec_msoa_residential': os.path.join(
             path, '01-validation_datasets', '02_subnational_elec', 'MSOA_domestic_electricity_2015_cleaned.csv'),
-        'path_val_subnational_elec_msoa_non_residential': os.path.join(
+        'val_subnational_elec_msoa_non_residential': os.path.join(
             path, '01-validation_datasets', '02_subnational_elec', 'MSOA_non_dom_electricity_2015_cleaned.csv'),
-        'path_val_subnational_gas': os.path.join(
+        'val_subnational_gas': os.path.join(
             path, '01-validation_datasets', '03_subnational_gas', 'data_2015_gas.csv'),
-        'path_val_subnational_gas_residential': os.path.join(
+        'val_subnational_gas_residential': os.path.join(
             path, '01-validation_datasets', '03_subnational_gas', 'data_2015_gas_domestic.csv'),
-        'path_val_subnational_gas_non_residential': os.path.join(
+        'val_subnational_gas_non_residential': os.path.join(
             path, '01-validation_datasets', '03_subnational_gas', 'data_2015_gas_non_domestic.csv'),
-        'path_val_nat_elec_data': os.path.join(
+        'val_nat_elec_data': os.path.join(
             path, '01-validation_datasets', '01_national_elec_2015', 'elec_demand_2015.csv')}
 
     return paths

energy_demand/result_processing/multiple_scenarios.py

@@ -43,15 +43,7 @@ def process_result_multi_scen(
         "plot_radar": True,
         "plot_radar_seasonal": True,                      # Plot radar spider charts
         "plot_line_for_every_region_of_peak_demand": True,
-        "plot_lad_cross_graphs": True,
-
-        # NEW
-        #"plot_weather_day_year": False,   #plot for a period (e.g. day) the hourly profile of all weather plots
-
-        # Specific figures
-        #"plot_figII_specific": False, #
-        #"plot_spatial_weather_var_peak": True #
-        }
+        "plot_lad_cross_graphs": True}
 
     # Get all folders with scenario run results (name of folder is scenario)
     scenarios = os.listdir(path_to_folder_with_scenarios)