updated Proj package

energy_demand/basic/basic_functions.py

@@ -138,9 +138,8 @@ def get_long_lat_decimal_degrees(reg_centroids):
     """
     reg_coord = {}
     for centroid in reg_centroids:
-
-        in_projection = Proj(init='epsg:27700') # OSGB_1936_British_National_Grid
-        put_projection = Proj(init='epsg:4326') # WGS 84 projection
+        in_projection = Proj(init="epsg:27700") # OSGB_1936_British_National_Grid
+        put_projection = Proj(init="epsg:4326") # WGS 84 projection
 
         # Convert to decimal degrees
         long_dd, lat_dd = transform(

energy_demand/main.py

@@ -90,7 +90,7 @@ def energy_demand_model(
 
     data = {}
     sim_yrs = [2015, 2020, 2025, 2030, 2035, 2040, 2045, 2050]
-    #sim_yrs = [2015, 2030, 2050]
+    sim_yrs = [2015, 2030, 2050]
 
 
     if len(sys.argv) > 3: #user defined arguments are provide
@@ -160,6 +160,7 @@ def energy_demand_model(
     # ----------------------------------------------------------------------
     # Load data
     # ----------------------------------------------------------------------
+    print("a " + str(name_region_set))
     data['scenario_data'] = defaultdict(dict)
     data['enduses'], data['sectors'], data['fuels'], lookup_enduses, lookup_sector_enduses = data_loader.load_fuels(data['paths'])
     data['regions'] = read_data.get_region_names(name_region_set)
@@ -340,11 +341,6 @@ def energy_demand_model(
     except:
         service_switches = []
 
-    #print("ddddddddddd")
-    print(service_switches_raw)
-    print(service_switches)
-    #raise Exception("TTT")
-
     fuel_switches = read_data.read_fuel_switches(os.path.join(path_strategy_vars, "switches_fuel.csv"), data['enduses'], data['assumptions'].fueltypes, data['assumptions'].technologies)
     capacity_switches = read_data.read_capacity_switch(os.path.join(path_strategy_vars, "switches_capacity.csv"))
 
@@ -543,6 +539,13 @@ def energy_demand_model(
                     sim_obj.ed_fueltype_regs_yh,
                     'fueltype_reg_8760')
 
+                # Write only peak day for space and water heating
+                write_data.write_space_and_water_heating(
+                    sim_yr,
+                    data['weather_yr_result_paths']['data_results_model_run_results_txt'],
+                    sim_obj.tot_fuel_y_enduse_specific_yh,
+                    "out_enduse_specific")
+
                 # PLot only residential total regional annual demand and
                 '''write_data.write_residential_tot_demands(
                     sim_yr,

energy_demand/read_write/write_data.py

@@ -466,6 +466,71 @@ def write_residential_tot_demands(
 
     np.save(path_file, tot_fuel_y_enduse_specific_yh)
 
+def write_space_and_water_heating(
+        sim_yr,
+        path_result,
+        tot_fuel_y_enduse_specific_yh,
+        filename
+    ):
+    """Write out enduse specific results for every hour and store to
+    `.npy` file
+
+    Arguments
+    -----------
+    sim_yr : int
+        Simulation year
+    path_result : str
+        Path
+    tot_fuel_y_enduse_specific_yh : dict
+        Modelling results
+    filename : str
+        File name
+    """
+    statistics_to_print = []
+    statistics_to_print.append("{}\t \t \t \t{}".format(
+        "Enduse", "total_annual_GWh"))
+
+    # Create folder for model simulation year
+    basic_functions.create_folder(path_result)
+
+    basic_functions.create_folder(
+        path_result, "enduse_specific_results")
+
+    for enduse, fuel in tot_fuel_y_enduse_specific_yh.items():
+        logging.info("   ... Enduse specific writing to file: %s  Total demand: %s ", enduse, np.sum(fuel))
+
+        if enduse in [
+            'rs_space_heating',
+            'ss_space_heating',
+            'is_space_heating',
+            'rs_water_heating',
+            'ss_water_heating']:
+
+            path_file = os.path.join(
+                os.path.join(path_result, "enduse_specific_results"),
+                "{}__{}__{}{}".format(
+                    filename,
+                    enduse,
+                    sim_yr,
+                    ".npy"))
+
+            np.save(path_file, fuel)
+
+            statistics_to_print.append("{}\t\t\t\t{}".format(
+                enduse, np.sum(fuel)))
+
+    # Create statistic files with sum of all end uses
+    path_file = os.path.join(
+        os.path.join(path_result, "enduse_specific_results"),
+        "{}__{}__{}".format(
+            "statistics_end_uses",
+            sim_yr,
+            ".txt"))
+
+    write_list_to_txt(
+        path_file,
+        statistics_to_print)
+
 def write_enduse_specific(
         sim_yr,
         path_result,