Implemented scenario drivers for esidential model. V2.1

data/residential_model/data_residential_by_fuel_end_uses.csv

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

@@ -1,5 +1,5 @@
-DAYTYP_ID,day_typ
-0,Weekd_day
-1,Weekend
-2,Coldest
-3,Warmest
+daytype_id,day_typ
+0,weekd_day
+1,weeknd
+2,coldest
+3,warmest

energy_demand/assumptions.py

@@ -151,7 +151,16 @@ def load_assumptions(data):
                                   '4' : 0.0,
                                   '5' : 0.0581,
                                   '6' : 0.0
-                                }
+                                },
+                    'heating': {
+                                  '0' : 0.2,
+                                  '1' : 0.3,
+                                  '2' : 0.5,
+                                  '3' : 0.0,
+                                  '4' : 0.0,
+                                  '5' : 0.0,
+                                  '6' : 0.0
+                                  }
                     }
 
     # Check if base demand is 100 %
@@ -219,7 +228,16 @@ def load_assumptions(data):
                                   '4' : 0.0,
                                   '5' : 0.0581,
                                   '6' : 0.0
-                                }
+                                },
+                    'heating': {
+                                  '0' : 0.2,
+                                  '1' : 0.3,
+                                  '2' : 0.5,
+                                  '3' : 0.0,
+                                  '4' : 0.0,
+                                  '5' : 0.0,
+                                  '6' : 0.0
+                                  }
                     }
 
     # Check if base demand is 100 %

energy_demand/building_stock_functions.py

@@ -35,6 +35,7 @@ def __init__(self, sim_y, coordinates, dwtype, house_id, age, pop, floorarea, HD
         self.pop = pop
         self.floorarea = floorarea
         self.HDD = get_HDD_based_on_int_temp(sim_y, assumptions, HDD) # Get internal temperature depending on assumptions of sim_year
+        #self.HOUSEHOLDINCOME?
 
     def scenario_driver_water_heating(self):
         """calc scenario driver with population and heat loss coefficient"""
@@ -56,6 +57,7 @@ def get_HDD_based_on_int_temp(sim_y, assumptions, HDD):
     #int_temp_sim_y = get_internal_temperature(sim_y)
     #HDD = recalculate_hitchens(int_temp_sim_y)
     HDD = "tbd"
+    HDD = 999
     # Recalcuulate heating degree days based on internal temperature change
     # Hitchins Formula
     return HDD
@@ -108,6 +110,12 @@ def __init__(self, region_ID, dwellings):
         self.region_ID = region_ID
         self.dwellings = dwellings
 
+        # Execute functions of stock
+        self.pop = self.get_tot_pop()
+        self.sd_water_heating = self.get_sum_scenario_driver_water_heating()
+        self.sd_heating = self.get_sum_scenario_driver_space_heating()
+        self.sd_lighting = self.get_sum_scenario_driver_lighting()
+
     def get_tot_pop(self):
         """Get total population of all dwellings"""
         totpop = 0
@@ -119,21 +127,21 @@ def get_sum_scenario_driver_water_heating(self):
         """Sum all scenario driver for water heating"""
         sum_driver = 0
         for dwelling in self.dwellings:
-            sum_driver += dwelling.scenario_driver_water_heating
+            sum_driver += dwelling.scenario_driver_water_heating()
         return sum_driver
 
     def get_sum_scenario_driver_space_heating(self):
         """Sum all scenario driver for space heating"""
         sum_driver = 0
         for dwelling in self.dwellings:
-            sum_driver += dwelling.scenario_driver_space_heating
+            sum_driver += dwelling.scenario_driver_space_heating()
         return sum_driver
 
     def get_sum_scenario_driver_lighting(self):
         """Sum all scenario driver for lighting heating"""
         sum_driver = 0
         for dwelling in self.dwellings:
-            sum_driver += dwelling.scenario_driver_lighting
+            sum_driver += dwelling.scenario_driver_lighting()
         return sum_driver
 
 def calc_floorarea_pp(reg_floorarea, reg_pop_by, glob_var, assump_final_diff_floorarea_pp):

energy_demand/building_stock_generator.py

@@ -53,16 +53,16 @@ def resid_build_stock(data, assumptions, data_ext):
     for reg_id in reg_lu:
 
         # Base year data of region
-        floorarea_by = data['reg_floorarea'][reg_id]      # Read in floor area of base year
-        pop_by = data_ext['population'][base_year][reg_id]                  # Read in population
-        floorarea_pp_by = floorarea_by / pop_by           # Floor area per person [m2/person]
+        floorarea_by = data['reg_floorarea'][reg_id]        # Read in floor area of base year
+        pop_by = data_ext['population'][base_year][reg_id]  # Read in population
+        floorarea_pp_by = floorarea_by / pop_by             # Floor area per person [m2/person]
 
         # Iterate simulation year
         for sim_y in sim_period:
 
             # Calculate new necessary floor area of simulation year
-            floorarea_pp_sy = data_floorarea_pp[reg_id][sim_y]                          #  Get floor area per person of sim_yr
-            tot_floorarea_sy = floorarea_pp_sy * data_ext['population'][sim_y][reg_id]  # floor area per person simulation year * population of simulation year in region
+            floorarea_pp_sy = data_floorarea_pp[reg_id][sim_y]                          # Get floor area per person of sim_yr
+            tot_floorarea_sy = floorarea_pp_sy * data_ext['population'][sim_y][reg_id]  # Floor area per person simulation year * population of simulation year in region
             new_floorarea_sim_year = tot_floorarea_sy - floorarea_by                    # tot new floor area - area base year
 
             # Only calculate changing
@@ -159,14 +159,14 @@ def generate_dw_existing(uniqueID, sim_y, dw_lu, floorarea_p, floorarea_by, dwty
     # Iterate dwelling types
     for dw in dw_lu:
         dw_type_id, dw_type_name = dw, dw_lu[dw]
-        dw_type_floorarea = floorarea_p[dw_type_name] * floorarea_by      # Floor area of existing buildlings
+        dw_type_floorarea = floorarea_p[dw_type_name] * floorarea_by            # Floor area of existing buildlings
 
         # Distribute according to age
         for dwtype_age_id in dwtype_age_distr_by:
             age_class_p = dwtype_age_distr_by[dwtype_age_id] / 100              # Percent of dw of age class
 
             # Floor area of dwelling_class_age
-            dw_type_age_class_floorarea = dw_type_floorarea * age_class_p     # Distribute proportionally floor area
+            dw_type_age_class_floorarea = dw_type_floorarea * age_class_p       # Distribute proportionally floor area
             control_floorarea += dw_type_age_class_floorarea
 
             # Pop

energy_demand/data_loader.py

@@ -0,0 +1,42 @@
+""" new load profile generator """
+import numpy as np
+import os
+import csv
+import main_functions as mf
+from datetime import date
+import unittest
+import matplotlib.pyplot as plt
+import data_loader_functions as df
+
+def generate_data(data, run_data_collection):
+    """ This function loads all that which does not neet to be run every time"""
+    base_year_load_data = 2015
+
+    if run_data_collection == False:
+        # TODO
+        # Read in dumped csv files
+        return data
+
+    # --------------
+    # HES data
+    # --------------
+    # Generate generic load profiles (shapes) for all electricity appliances from HES data [ % ]
+    hes_data, hes_y_peak, _ = df.read_hes_data(data)
+    year_raw_values_hes = df.assign_hes_data_to_year(data, hes_data, base_year_load_data)
+
+    # Load shape for all end_uses
+    for i in data['data_residential_by_fuel_end_uses']:
+        end_use = i # End use read from avaialble fuels...
+        data = df.get_hes_end_uses_shape(data, hes_data, year_raw_values_hes, hes_y_peak, _, end_use)
+
+    # Dump created end_use_dictionaries into txt files #TODO
+
+    # ----------------------------
+    # Residential Gas demand ()
+    # Daily shapes taken from Robert Sansom
+    # Yearly peak from CSWV
+    # ----------------------------
+    end_use = 'heating'
+    data = df.shape_residential_heating_gas(data, end_use)
+
+    return data