working on shift

energy_demand/enduse_func.py

@@ -4,6 +4,7 @@
 import logging
 import math
 import numpy as np
+import matplotlib.pyplot as plt
 
 from energy_demand.basic import date_prop
 from energy_demand.profiles import load_factors as lf
@@ -235,6 +236,20 @@ def __init__(
                         mode_constrained=False,
                         make_all_flat=make_all_flat)
                     #print("FUEL TRAIN Y" + str(np.sum(fuel_yh)))
+
+                    '''print("-----otside-------------")
+                    if curr_yr > 2015 and enduse == 'rs_space_heating':
+                        plt.plot(self.fuel_yh.reshape(8760), label="after")
+                        plt.plot(fuel_yh.reshape(8760), label="before")
+                        plt.legend()
+                        plt.show()
+                        print("----")'''
+
+                    #if curr_yr > 2015:
+                    #    _a = np.max(fuel_yh)
+                    #    _b = np.max(self.fuel_yh)
+                    #    print("AFTER SHIFTING : {} {}".format(_a, _b))
+
             else:
                 #If technologies are defined for an enduse
 
@@ -400,18 +415,9 @@ def load_shifting(
     """
     # Get assumed load shift
     if dm_improvement[enduse][curr_yr] == 0:
-
-        if curr_yr >= 2015:
-            print(fuel_yh.shape)
-            _sum_dayily = np.sum(fuel_yh, axis=0)
-            _peak_day = np.argmax(_sum_dayily)
-            print("aa " + str(_peak_day))
-            print(fuel_yh[int(_peak_day)])
-            __max_after = fuel_yh.argmax(fuel_yh[int(_peak_day)])
-            print("peak zero {}".format(__max_after))
-
         pass # no load management
     else:
+
         # Calculate average for every day
         if mode_constrained:
             average_fuel_yd = np.average(fuel_yh, axis=1)
@@ -424,39 +430,54 @@ def load_shifting(
 
         # Load factor improvement parameter in current year
         param_lf_improved_cy = dm_improvement[enduse][curr_yr]
+        #param_lf_improved_cy = 0.1
+        print("Loading shifting .. {}  {}  {}".format(enduse, curr_yr, param_lf_improved_cy))
 
         # Calculate current year load factors
         lf_improved_cy = calc_lf_improvement(
             param_lf_improved_cy,
             loadfactor_yd_cy)
 
-        import copy
-        _a = copy.copy(fuel_yh)
+        #import copy
+        #_a = copy.copy(fuel_yh)
 
         fuel_yh = lf.peak_shaving_max_min(
             lf_improved_cy,
             average_fuel_yd,
             fuel_yh,
             mode_constrained)
 
-
-        #'''
-        import matplotlib.pyplot as plt
-        print(fuel_yh.shape)
-
-        plt.plot(fuel_yh[10], label="after")
-        plt.plot(_a[10], label="before")
-        plt.legend()
-        plt.show()
-        print("----")
-
-        # Get maximum hour for electricity demand
-        _sum_dayily = np.sum(fuel_yh, axis=0)
-        _peak_day = np.argmax(_sum_dayily)
-        __max_after = fuel_yh.argmax(fuel_yh[_peak_day])
-        __max_before = fuel_yh.argmax(_a[_peak_day])
-        print("befire; {} after: {}".format(__max_before, __max_after))
-        #'''
+    '''
+        if enduse == "rs_space_heating":
+            print("ENDUSE " + str(enduse))
+            print(curr_yr)
+            print(fuel_yh.shape)
+            _sum_dayily = np.sum(fuel_yh, axis=1)
+            print(_sum_dayily.shape)
+            _peak_day = np.argmax(_sum_dayily)
+            print(_peak_day)
+            print(lf_improved_cy[_peak_day])
+            #plt.plot(fuel_yh[_peak_day], label="after")
+            #plt.plot(_a[_peak_day], label="before")
+            plt.plot(fuel_yh.reshape(8760), label="after")
+            plt.plot(_a.reshape(8760), label="before")
+            plt.legend()
+            plt.show()
+            print("----")
+
+
+            print(param_lf_improved_cy)
+            print(loadfactor_yd_cy[_peak_day])
+            print(lf_improved_cy[_peak_day])
+            
+            # Get maximum hour for electricity demand
+            __max_after = np.max(fuel_yh[_peak_day])
+            __max_before = np.max(_a[_peak_day])
+            print("before; {} after: {}".format(__max_before, __max_after))
+            _new_lf = lf.calc_lf_y_8760(fuel_yh[_peak_day])
+            _old_lf = lf.calc_lf_y_8760(_a[_peak_day])
+            print("before; {} after: {}".format(_old_lf, _new_lf))
+    '''
 
     # -------------------------------------------------
     # Convert all load profiles into flat load profiles
@@ -497,10 +518,9 @@ def calc_lf_improvement(
         True: Peak is shifted, False: Peak isn't shifed
     """
     # Add load factor improvement to current year load factor
-    lf_improved_cy = loadfactor_yd_cy + param_lf_improved_cy #* 100
+    lf_improved_cy = loadfactor_yd_cy + param_lf_improved_cy
 
     # Where load factor larger than zero, set to 1
-    #lf_improved_cy[lf_improved_cy > 1] = 100
     lf_improved_cy[lf_improved_cy > 1] = 1
 
     return lf_improved_cy

energy_demand/main.py

@@ -97,13 +97,13 @@ def energy_demand_model(
     data = {}
     sim_yrs = [2015, 2020, 2025, 2030, 2035, 2040, 2045, 2050]
     #sim_yrs = [2015, 2020, 2050] #, 2050]
-    sim_yrs = [2015, 2050]
+    sim_yrs = [2015, 2030, 2050]
 
     if len(sys.argv) > 3: #user defined arguments are provide
         scenario_name = str(sys.argv[2])
         weather_realisation = str(sys.argv[3]) # Weather realisation 
     else:
-        scenario_name = "_dm_0_"
+        scenario_name = "_dm_0P_"
         weather_realisation = 'NF1'
 
     print("-------------------------------------")

energy_demand/model.py

@@ -175,19 +175,6 @@ def aggregate_across_all_regs(
     # -----------------------------
     # Aggregate residential demand [fueltype, region]
     # -----------------------------
-    '''array_init = np.zeros((fueltypes_nr, reg_nrs)) #Annual
-    for submodel in aggr_results['ed_submodel_enduse_fueltype_regs_yh']:
-
-        if submodel == 0:
-            for enduse in aggr_results['ed_submodel_enduse_fueltype_regs_yh'][submodel]:
-
-                enduse_array_nr = lookup_enduses[enduse]
-                
-                # Sum across all hours in a year
-                array_init += np.sum(aggr_results['ed_submodel_enduse_fueltype_regs_yh'][submodel][enduse], axis=2)
-        else:
-            pass
-    '''
     fueltypes = lookup_tables.basic_lookups()['fueltypes']
     submodel_nr = 0
     array_init = np.zeros((fueltypes_nr, reg_nrs))

energy_demand/plotting/fig_3_plot_over_time.py

@@ -51,14 +51,14 @@ def scenario_over_time(
     """
     statistics_to_print = []
 
-    fig = plt.figure(
-        figsize=basic_plot_functions.cm2inch(10, 10)) #width, height
+    fig = plt.figure(figsize=basic_plot_functions.cm2inch(10, 10)) #width, height
 
     ax = fig.add_subplot(1, 1, 1)
 
     for cnt_scenario, i in enumerate(scenario_result_container):
         scenario_name = i['scenario_name']
         national_peak = i['national_peak']
+
         # dataframe with national peak (columns= simulation year, row: Realisation) 
         # Calculate quantiles
         quantile_95 = 0.95

energy_demand/profiles/load_factors.py

@@ -118,7 +118,7 @@ def calc_lf_y(fuel_yh):
     Returns
     -------
     load_factor_y : array
-        Yearly load factors as percentage (100% = 100)
+        Yearly load factors as percentage (100% = 1)
 
     Note
     -----
@@ -158,7 +158,7 @@ def calc_lf_y_8760(fuel_yh_8760):
     Returns
     -------
     load_factor_y : array
-        Yearly load factors as percentage (100% = 100)
+        Yearly load factors as percentage (100% = 1)
 
     Note
     -----
@@ -167,7 +167,7 @@ def calc_lf_y_8760(fuel_yh_8760):
     https://circuitglobe.com/load-factor.html
     """
     # Get total sum per fueltype
-    tot_load_y = np.sum(fuel_yh_8760) #, axis=2)
+    tot_load_y = np.sum(fuel_yh_8760)
 
     # Calculate maximum hour in every day of a year
     max_load_h = np.max(fuel_yh_8760)
@@ -178,9 +178,8 @@ def calc_lf_y_8760(fuel_yh_8760):
 
     if np.isnan(load_factor_y):
         load_factor_y = 0
-    #load_factor_y[np.isnan(load_factor_y)] = 0
 
-    return load_factor_y #* 100
+    return load_factor_y
 
 def calc_lf_season(seasons, fuel_region_yh, average_fuel_yd):
     """Calculate load factors per fueltype per region.
@@ -223,8 +222,8 @@ def calc_lf_season(seasons, fuel_region_yh, average_fuel_yd):
         # Unable local RuntimeWarning: divide by zero encountered
         with np.errstate(divide='ignore', invalid='ignore'):
 
-            #convert to percentage
-            season_lf = (average_fuel_yd_full_year / max_load_h_season) #* 100
+            #convert to decimal
+            season_lf = (average_fuel_yd_full_year / max_load_h_season)
 
         # Replace
         season_lf[np.isinf(season_lf)] = 0
@@ -264,8 +263,8 @@ def calc_lf_d(fuel_yh, average_fuel_yd, mode_constrained):
     # Unable local RuntimeWarning: divide by zero encountered
     with np.errstate(divide='ignore', invalid='ignore'):
 
-        #convert to percentage
-        daily_lf = (average_fuel_yd / max_load_yd) #* 100
+        #convert to decimal
+        daily_lf = (average_fuel_yd / max_load_yd)
 
     # Replace by zero
     daily_lf[np.isinf(daily_lf)] = 0
@@ -296,15 +295,15 @@ def calc_lf_d_8760(fuel_yh):
     """
     # Get maximum hours in every day
     fuel_yh = fuel_yh.reshape(365, 24)
-    
+
     max_load_yd = np.max(fuel_yh, axis=1)
     average_fuel_yd = np.average(fuel_yh, axis=1)
 
     # Unable local RuntimeWarning: divide by zero encountered
     with np.errstate(divide='ignore', invalid='ignore'):
 
-        #convert to percentage
-        daily_lf = (average_fuel_yd / max_load_yd) #* 100
+        #convert to decimal
+        daily_lf = (average_fuel_yd / max_load_yd)
 
     # Replace by zero
     daily_lf[np.isinf(daily_lf)] = 0
@@ -349,7 +348,7 @@ def calc_lf_season_8760(seasons, fuel_region_yh):
 
         # Unable local RuntimeWarning: divide by zero encountered
         with np.errstate(divide='ignore', invalid='ignore'):
-            season_lf = (average_fuel_yd / max_load_h_season) #* 100 #convert to percentage
+            season_lf = (average_fuel_yd / max_load_h_season)
 
         # Replace
         season_lf[np.isinf(season_lf)] = 0

energy_demand/read_write/read_weather_results.py

@@ -39,7 +39,6 @@ def read_in_weather_results(
         path_result, 'only_total')
 
     #print(results_container['ed_reg_tot_y'][2015].shape)
-
     results_container['ed_reg_peakday'] = read_data.read_results_yh(
         os.path.join('simulation_results', path_result), 'only_peak')
 

energy_demand/result_processing/pII_weather_variability_plots.py

@@ -34,6 +34,7 @@ def main(
         Year to generate comparison plots
     """
     print("Start creating plots")
+
     # -------------------
     # Create result folder
     # -------------------
@@ -205,8 +206,7 @@ def main(
             seperate_legend=False)
 
         # ------------------------------
-        # Plot national peak change over time for each scenario
-        # including weather variability
+        # Plot national peak change over time for each scenario including weather variability
         # ------------------------------
         fig_3_plot_over_time.scenario_over_time(
             scenario_result_container=scenario_result_container,
@@ -290,6 +290,7 @@ def main(
 path_shapefile_input="C:/Users/cenv0553/ED/data/energy_demand/region_definitions/lad_2016_uk_simplified.shp"
 
 main(
+    #scenarios_path="C:/_FFF_local", #_local",
     scenarios_path="C:/_FFF",
     path_shapefile_input=path_shapefile_input,
     base_yr=2015,

tests/test_enduse_func.py

@@ -10,7 +10,7 @@
 def test_enduse():
     """testing"""
     out = enduse_func.Enduse(
-        submodel_name ='test_name',
+        submodel_name='test_name',
         region='test',
         scenario_data='test',
         assumptions='test',