Deleted main parts of old model. Goold Version to Fall back....

data/residential_model/__data_residential_by_fuel_end_uses.csv

@@ -0,0 +1,8 @@
+fuel_id,light,cold,wet,consumer_electronics,home_computing,cooking,heating
+0,0,0,0,0,0,0,100
+1,0,0,0,0,0,0,100
+2,1002,1037,1306,1838,549,1138,0
+3,0,0,0,0,0,0,100
+4,0,0,0,0,0,0,100
+5,0,0,0,0,0,0,0
+6,0,0,0,0,0,0,0

data/residential_model/data_residential_by_fuel_end_uses.csv

@@ -1,8 +1,8 @@
-fuel_id,light,cold,wet,consumer_electronics,home_computing,cooking,heating
-0,0,0,0,0,0,0,100
-1,0,0,0,0,0,0,100
-2,1002,1037,1306,1838,549,1138,0
-3,0,0,0,0,0,0,100
-4,0,0,0,0,0,0,100
-5,0,0,0,0,0,0,0
-6,0,0,0,0,0,0,0
+fuel_id,light
+0,100
+1,100
+2,1002
+3,100
+4,100
+5,100
+6,100

energy_demand/building_stock_generator.py

@@ -82,23 +82,23 @@ def resid_build_stock(data, assumptions, data_ext):
         reg_building_stock_cur_yr[reg_id] = bf.DwStockRegion(reg_id, dw_stock_new_dwellings)    # Add old and new buildings to stock
         reg_building_stock_by[reg_id] = bf.DwStockRegion(reg_id, dw_stock_base)                 # Add base year stock
 
-    print("Base dwelling")
-    print(reg_building_stock_by[0].get_tot_pop())
+    #print("Base dwelling")
+    #print(reg_building_stock_by[0].get_tot_pop())
     l = reg_building_stock_by[0].dwellings
-    for i in l:
-        print(i.__dict__)
+    #for i in l:
+    #    print(i.__dict__)
 
-    print("Curryear dwelling")
-    print(reg_building_stock_cur_yr[0].get_tot_pop())
+    ##print("Curryear dwelling")
+    #print(reg_building_stock_cur_yr[0].get_tot_pop())
     l = reg_building_stock_cur_yr[0].dwellings
-    for i in l:
-        print(i.__dict__)
+    #for i in l:
+    #    print(i.__dict__)
 
     # Add to data
     data['reg_building_stock_by'] = reg_building_stock_by
     data['reg_building_stock_cur_yr'] = reg_building_stock_cur_yr
 
-    print(" -- Virtual Building Stock generated")
+    #print(" -- Virtual Building Stock generated")
     return data
 
 def p_floorarea_dwtype(dw_lookup, dw_floorarea_by, dwtype_distr_sim):

energy_demand/main.py

@@ -56,82 +56,35 @@ def energy_demand_model(data, assumptions, data_ext):
     All messy now...needs cleaning
     """
 
-    # NEW MODEL HERE
-    #import energy_demand.main_object_approach as NEWMODEL
-    #NEWMODEL.new_energy_demand_model(data, data_ext, assumptions)
     tech_stock = ts.resid_tech_stock(data_ext['glob_var']['current_year'], data, assumptions, data_ext) #TODO ASSUMPTIONS
     data['tech_stock'] = tech_stock
 
-    # Iterate REGION AND GENERATE OBJECTS
-    result_dict = {1: {}, 2: {}} #Number of fueltypes #TODO: Improve
+    # Iterate regions and generate objects
+    timesteps, _ = mf.timesteps_full_year()                                 # Create timesteps for full year (wrapper-timesteps)
+    result_dict = mf.init_dict_energy_supply(data['fuel_type_lu'], data['reg_pop_array'], timesteps)
+
+    all_Regions = []
 
     for reg in data['reg_lu']:
-        reg_dict = {}
-        print("Region: " + str(reg))
 
         # Residential
         a = mm.Region(reg, data, data_ext, assumptions)
-        hourly_all_fuels = a.tot_all_enduses_h()
-
-        gas_final = hourly_all_fuels[1]
-        elec = hourly_all_fuels[2]
-
-        # Convert output into dict
-        out_dict_gas = dict(enumerate(gas_final))   # Convert array into dict for out_read
-        for i in out_dict_gas:
-                new = dict(enumerate(out_dict_gas[i]))
-                out_dict_gas[i] = new
-
-        out_dict_elec = dict(enumerate((elec)))      # Convert array into dict for out_read
-        for i in out_dict_elec:
-                new = dict(enumerate(out_dict_elec[i]))
-                out_dict_elec[i] = new
+        all_Regions.append(a)
 
-        result_dict[1][reg] = out_dict_gas
-        result_dict[2][reg] = out_dict_elec
+    result_dict = mf.convert_result_to_final_total_format(data, all_Regions)
 
 
-
-    '''# OLD MODEL BELOW
-    # Convert population data int array
-    data_ext = mf.add_to_data(data_ext, data_ext['population']) # Convert to array, store in data
-
-    # Run different sub-models (sector models)
-
-    # -- Residential model
-    e_app_bd, g_hd_bd = residential_model.run(data_ext['glob_var'], data['shape_app_elec'], data['reg_pop_array'], data_ext['reg_pop_external_array'], data['timesteps_app_bd'], data['timesteps_hd_bd'])
-
-    '''
-    ''' # ---------------------------------------------------------------------------
-    # Generate the wrapper timesteps and add instert data (from own timeperiod to full year)
-    print("Generate resutls for wrapper (read into nested dictionary")
-    # ---------------------------------------------------------------------------
-    timesteps, _ = mf.timesteps_full_year()                                 # Create timesteps for full year (wrapper-timesteps)
-
-    # Initialise nested Dicionatry for wrapper (Fuel type, region, hour)
-    result_dict = mf.init_dict_energy_supply(data['fuel_type_lu'], data['reg_pop_array'], timesteps)
-
-    # Add electricity data to result dict for wrapper
-    #result_dict = mf.add_demand_result_dict(0, e_app_bd, data['fuel_type_lu'], data['reg_pop_array'], timesteps, result_dict, data['timesteps_own_selection'])
-    result_dict = mf.add_demand_result_dict(0, e_app_bd, data['fuel_type_lu'], data['reg_pop_array'], timesteps, result_dict, data['timesteps_own_selection'])
-    #print(result_dict[0][0])
-
-    # Add gas data
-    #result_dict = mf.add_demand_result_dict(1, g_hd_bd, data['fuel_type_lu'], data['reg_pop_array'], timesteps, result_dict, data['timesteps_own_selection'])
-    result_dict = mf.add_demand_result_dict(1, g_hd_bd, data['fuel_type_lu'], data['reg_pop_array'], timesteps, result_dict, data['timesteps_own_selection'])
-    '''
     # Write YAML File
     #mf.write_YAML(False, 'C:/Users/cenv0553/GIT/NISMODII/TESTYAML.yaml')
 
     # --- Write out functions....scrap to improve
-    mf.write_to_csv_will(result_dict, data['reg_lu']) #TODO IMprove
+    mf.write_to_csv_will(data, result_dict, data['reg_lu']) #TODO IMprove
 
     # Write function to also write out results
     print("FINAL Fueltype:  " + str(len(result_dict)))
     print("FINAL region:    " + str(len(result_dict[1])))
     print("FINAL timesteps: " + str(len(result_dict[1][0])))
     print("Finished energy demand model")
-    #print(result_dict)
     return result_dict
 
 
@@ -154,18 +107,13 @@ def energy_demand_model(data, assumptions, data_ext):
     # Model calculations outside main function
     path_main = r'C:/Users/cenv0553/GIT/NISMODII/data/'                        # #path_main = '../data'
     base_data = mf.load_data(base_data, data_external, path_main)              # Load and generate data
-
-    base_data = mf.OLDMODEL_load_data(base_data, data_external, path_main)     # DELETE SOON
-
-
     assumptions_model_run = assumpt.load_assumptions(base_data)                # Load assumptions
     base_data = mf.disaggregate_base_demand_for_reg(base_data, 1)              # Disaggregate national data into regional data
 
     # Generate virtual building stock over whole simulatin period
     base_data = bg.resid_build_stock(base_data, assumptions_model_run, data_external)
 
 
-
     # Generate technological stock over whole simulation period
     #base_tech_stock_resid = ts.resid_tech_stock(2015, assumptions_model_run, data_external)
 

energy_demand/main_functions.py

@@ -4,7 +4,7 @@
 import os
 import csv
 import traceback
-import datetime
+
 from datetime import date
 from datetime import timedelta as td
 import numpy as np
@@ -15,6 +15,32 @@
 #from datetime import datetime
 #import datetime
 
+
+def convert_result_to_final_total_format(data, all_Regions):
+    """Convert into nested citionary with fueltype, region, hour"""
+
+    timesteps, _ = timesteps_full_year()                                 # Create timesteps for full year (wrapper-timesteps)
+    result_dict = init_dict_energy_supply(data['fuel_type_lu'], data['reg_pop_array'], timesteps)
+
+    for reg in all_Regions:
+        print("Reg_NAme " + str(reg))
+        region_name = reg.reg_name
+
+        # Get total fuel fuel
+        hourly_all_fuels = reg.tot_all_enduses_h()
+
+        # Iterate fueltypes
+        for fueltype in data['fuel_type_lu']:
+
+            fuel_total_h_all_end_uses = hourly_all_fuels[fueltype]
+
+            # Convert array into dict for out_read
+            out_dict = dict(enumerate(fuel_total_h_all_end_uses))
+
+            result_dict[fueltype][region_name] = out_dict
+
+    return result_dict
+
 def load_data(data, data_ext, path_main):
     """All base data no provided externally are loaded
 
@@ -48,9 +74,6 @@ def load_data(data, data_ext, path_main):
 
     return data
 
-
-
-
 def init_dict_energy_supply(fuel_type_lu, reg_pop, timesteps):
     """Generates nested dictionary for providing results to smif
 
@@ -190,17 +213,8 @@ def read_csv_base_data_resid(path_to_csv):
 
             cnt_fueltype += 1
 
-    #import pprint
-    '''for i in end_uses_dict:
-        print("Appliances"  + str(i))
-        print(end_uses_dict[i])
-        print("--")
-    '''
     return end_uses_dict
 
-
-
-
 def create_timesteps_app(fuel_type, date_list, bd_app_elec, reg_lu, fuel_type_lu, app_type_lu, timestep_dates):
     """Creates the timesteps for which the energy demand of the appliances is calculated.
     Then base energy demand is added for each timestep read in from yearly demand aray.
@@ -715,11 +729,13 @@ def timesteps_full_year():
     Parameters
     ----------
 
+
     Returns
     -------
     timestep : list
         List containing all dates according to number of hours
     """
+    #TODO: improve
 
     full_year_date = [date(2015, 1, 1), date(2015, 12, 31)] # Base Year
     start_date, end_date = full_year_date[0], full_year_date[1]
@@ -856,7 +872,7 @@ def get_own_position(daytype, _season, hour_container, timesteps_own_selection):
 
     return position_own_container
 
-def add_demand_result_dict(fuel_type, e_app_bd, fuel_type_lu, reg_pop, timesteps, result_dict, timesteps_own_selection):
+'''def add_demand_result_dict(fuel_type, e_app_bd, fuel_type_lu, reg_pop, timesteps, result_dict, timesteps_own_selection):
     """Add data to wrapper timesteps
 
     """
@@ -906,7 +922,7 @@ def add_demand_result_dict(fuel_type, e_app_bd, fuel_type_lu, reg_pop, timesteps
                 year_hour += 1
 
     return result_dict
-
+'''
 def get_own_timesteps(date_list):
     """Create own timesteps. "Generets a list with all dates from a list containing start and end dates.
 
@@ -1145,7 +1161,7 @@ def read_data(data, path_main):
     app_type_lu = read_csv(path_dict['path_lookup_appliances'])                   # Appliances types lookup table
     #fuel_type_lu = read_csv(path_dict['path_fuel_type_lu'])                       # Fuel type lookup
     fuel_type_lu = read_csv_dict_no_header(path_dict['path_fuel_type_lu'])                       # Fuel type lookup
-    
+
     day_type_lu = read_csv(path_dict['path_day_type_lu'])                         # Day type lookup
     #season_lookup = read_csv(path_dict[]'path_season's_lookup'])                 # Season lookup
 
@@ -1200,8 +1216,8 @@ def read_data(data, path_main):
     data['lu_appliances_HES_matched'] = lu_appliances_HES_matched
 
     # load shapes
-    data['dict_shapes_end_use_h'] = {} 
-    data['dict_shapes_end_use_d'] = {} 
+    data['dict_shapes_end_use_h'] = {}
+    data['dict_shapes_end_use_d'] = {}
     return data
 
 def disaggregate_base_demand_for_reg(data, reg_data_assump_disaggreg):
@@ -1271,34 +1287,35 @@ def write_YAML(yaml_write, path_YAML):
     return
 
 
-def write_to_csv_will(reesult_dict, reg_lu):
+def write_to_csv_will(data, reesult_dict, reg_lu):
     """ Write reults for energy supply model
     e.g.
 
     england, P0H, P1H, 139.42, 123.49
 
     """
 
-    path_csvs = ['C:/Users/cenv0553/GIT/NISMODII/model_output/NEU_elec.csv', 'C:/Users/cenv0553/GIT/NISMODII/model_output/NEU_gas.csv']
-    fueltype = 1
-    for path in path_csvs:
-        print("path: " + str(path))
-        print("--------------------")
-        #yaml_list = []
-        with open(path, 'w', newline='') as fp:
+    for fueltype in data['fuel_type_lu']:
 
+        #TODO: Give relative path stored in data[pathdict]
+        path = 'C:/Users/cenv0553/GIT/NISMODII/model_output/_fueltype_{}_hourly_results'.format(fueltype)
+
+        yaml_list = []
+        with open(path, 'w', newline='') as fp:
             a = csv.writer(fp, delimiter=',')
             data = []
-            #for i in range(len(reesult_dict)): # Iterate over fuel type
 
             for reg in reesult_dict[fueltype]:
                 region_name = reg_lu[reg]
 
+
                 for _day in reesult_dict[fueltype][reg]:
-                    for _hour in reesult_dict[fueltype][reg][_day]:
+                    for _hour in range(24):
+
                         start_id = "P{}H".format(_day * 24 + _hour)
                         end_id = "P{}H".format(_day * 24 + _hour + 1)
-                        #yaml_list.append({'region': region_name, 'start': _start, 'end': _end, 'value': reesult_dict[cnt][reg][_hour], 'units': 'GWh', 'year': 'XXXX'})
+                        yaml_list.append({'region': region_name, 'start': start_id, 'end': end_id, 'value': reesult_dict[fueltype][reg][_day][_hour], 'units': 'CHECK GWH', 'year': 'XXXX'})
+
                         data.append([region_name, start_id, end_id, reesult_dict[fueltype][reg][_day][_hour]])
 
             a.writerows(data)
@@ -1349,14 +1366,12 @@ def calc_daily_load_factor(daily_loads):
 '''
 
 def calc_peak_from_average(daily_loads):
-
-    # 
     max_load = average_load / load_factor
 
     return max_load
 
 
-def OLDMODEL_load_data(data, data_ext, path_main):
+'''def OLDMODEL_load_data(data, data_ext, path_main):
     
 
     # ----------below old model
@@ -1412,7 +1427,7 @@ def OLDMODEL_load_data(data, data_ext, path_main):
     print(" ")
 
     return data
-
+'''
 
 """A one-line summary that does not use variable names or the
     function name.
@@ -1466,4 +1481,4 @@ def OLDMODEL_load_data(data, data_ext, path_main):
     This can have multiple paragraphs.
     You may include some math:
 
-"""
+"""