bug fix main

energy_demand/building_stock_generator.py

@@ -1,5 +1,4 @@
-import sys
-import os
+import sys, os
 from pprint import pprint
 import building_stock_functions as bf
 from main_functions import read_csv
@@ -25,32 +24,35 @@
 dw_lookup = np.array(([0, 'Detached'], [1, 'Semi-Detached'], [2, 'Terraced (mid_end)'], [3, 'Flat'], [4, 'Bungalow']))
 
 # Assumptions
-pop2015_by = 100     # [person]
-floor_area_by = 2000 # [m2]
-nr_dw = 55        # [nr of buildings]
-pop2015_new = 200     # [person]
+pop2015_by = 100        # [person]
+floor_area_by = 2000    # [m2]
+nr_dw = 55              # [nr of buildings]
+pop2015_new = 200       # [person]
 
-# Factors to calculate
+# Derived factors
 floor_area_per_person_base_year_by = floor_area_by / pop2015_by # [m2/person] Floor area per person
-floor_area_per_building_by = floor_area_by / nr_dw  # [m2/dw] meter per building
-persons_per_build = pop2015_by / nr_dw     # [pers/build]
-
+floor_area_per_building_by = floor_area_by / nr_dw              # [m2/dw] meter per building
+persons_per_build = pop2015_by / nr_dw                          # [pers/build]
 
 
 # -----------Calculate the share of total floor area beloinging to each dwelling type-----------
+def get_percent_floor_area_per_dw_type():
+
+    return 
+
 total_floor_area = 0
 dw_floor_area_percent = {} # initialise
 
 for row in dw_lookup:
     # Get number of building of dwellin type
     percent_buildings_dw = (dw_dist[2015][int(row[0])])/100
-    nr_dw_typXY = nr_dw * percent_buildings_dw 
+    nr_dw_typXY = nr_dw * percent_buildings_dw
 
     # absolute usable floor area per dwelling type
     fl_type = nr_dw_typXY * dw_floor_area[int(row[0])]
 
     # sum total area
-    total_floor_area += fl_type 
+    total_floor_area += fl_type
     dw_floor_area_percent[int(row[0])] = fl_type # add absolute are ato dict
 
 # Convert absolute values into percentages
@@ -92,7 +94,6 @@
 
         # Share of population for this dwelling type (distribute pop according to floor area)
         _pop_2015_dwelling_type = floor_area_share_dw_of_ttal / floor_area_per_person_base_year_by
-
         print("_pop_2015_dwelling_type " + str(_pop_2015_dwelling_type))
 
         # Age class (because only comparison within the same dwelling class, we can redistribute buildlings with floor area)

energy_demand/main.py

@@ -19,7 +19,7 @@
 
 #!python3.6
 
-#TODOs data Different appliances for cold/hot extremes, data Heating fro min_max climate
+# NOCHT TUN ata Different appliances for cold/hot extremes, data Heating fro min_max climate
 
 import sys
 import os
@@ -49,13 +49,13 @@ def load_data():
 
     """
     # Global variables
-    YEAR_SIMULATION = 2015                                                  # Provide year for which to run the simulation
+    #YEAR_SIMULATION = 2015                                                  # Provide year for which to run the simulation
     P1_YEAR_BASE = 2015                                                     # [int] First year of the simulation period
-    P2_YEAR_END = 2050                                                      # [int] Last year of the simulation period
-    P3_SIM_PERIOD = range(P2_YEAR_END - P1_YEAR_BASE)                       # List with simulation years
-    P0_YEAR_CURR = YEAR_SIMULATION - P1_YEAR_BASE                           # [int] Current year in current simulation
-    SIM_PARAM = [P0_YEAR_CURR, P1_YEAR_BASE, P2_YEAR_END, P3_SIM_PERIOD]    # Store all parameters in one list
-
+    #P2_YEAR_END = 2050                                                      # [int] Last year of the simulation period
+    #P3_SIM_PERIOD = range(P2_YEAR_END - P1_YEAR_BASE)                       # List with simulation years
+    #P0_YEAR_CURR = YEAR_SIMULATION - P1_YEAR_BASE                           # [int] Current year in current simulation
+    #SIM_PARAM = [P0_YEAR_CURR, P1_YEAR_BASE, P2_YEAR_END, P3_SIM_PERIOD]    # Store all parameters in one list
+    SIM_PARAM = [0, 2015, 2050, range(50)]    # Store all parameters in one list
 
 
     #------Store all paths to data in dict-------------------
@@ -109,12 +109,10 @@ def load_data():
     own_timesteps = mf.own_timesteps(timesteps_own_selection)
 
     # Populate timesteps base year data (appliances, electricity)
-    fuel_type = 0 # elec
-    timesteps_app_bd = mf.create_timesteps_app(timesteps_own_selection, fuel_type, bd_app_elec, data['reg_lu'], data['fuel_type_lu'], data['app_type_lu'], own_timesteps) # [GWh]
+    timesteps_app_bd = mf.create_timesteps_app(0, timesteps_own_selection, bd_app_elec, data['reg_lu'], data['fuel_type_lu'], data['app_type_lu'], own_timesteps) # [GWh]
 
     # Populate timesteps base year data (heating demand, ga)
-    fuel_type = 1 #gas
-    timesteps_hd_bd = mf.create_timesteps_hd(timesteps_own_selection, fuel_type, bd_hd_gas, data['reg_lu'], data['fuel_type_lu'], own_timesteps) # [GWh]
+    timesteps_hd_bd = mf.create_timesteps_hd(1, timesteps_own_selection, bd_hd_gas, data['reg_lu'], data['fuel_type_lu'], own_timesteps) # [GWh]
 
     print("----------------------Statistics--------------------")
     print("Number of timesteps appliances:          " + str(len(timesteps_app_bd[0][0])))
@@ -216,17 +214,10 @@ def energy_demand_model(data, pop_data_external):
     # Add gas data
     result_dict = mf.add_demand_result_dict(1, g_hd_bd, data['fuel_type_lu'], reg_pop, timesteps, result_dict, data['timesteps_own_selection'])
 
-    # Write YAML file
-    yaml_write = False
-    if yaml_write: # == True:
-        import yaml
-        _, yaml_list = mf.timesteps_full_year()                                 # Create timesteps for full year (wrapper-timesteps)
-        path_YAML = 'C:/Users/cenv0553/GIT/NISMODII/TESTYAML.yaml'     # l = [{'id': value, 'start': 'p', 'end': 'P2',   }
-        with open(path_YAML, 'w') as outfile:
-            yaml.dump(yaml_list, outfile, default_flow_style=False)
+    # Write YAML File
+    #mf.write_YAML(False, 'C:/Users/cenv0553/GIT/NISMODII/TESTYAML.yaml')
 
     # Write function to also write out results
-
     print("FINAL Fueltype:  " + str(len(result_dict)))
     print("FINAL region:    " + str(len(result_dict[0])))
     print("FINAL timesteps: " + str(len(result_dict[0][0])))

energy_demand/main_functions.py

@@ -167,16 +167,16 @@ def get_dates_datelist(date_list):
                 timestep_dates.append(j)
     return timestep_dates
 
-def create_timesteps_app(date_list, fuel_type, bd_app_elec, reg_lu, fuel_type_lu, app_type_lu, timestep_dates):
+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.
 
     Parameters
     ----------
-    date_list : list
-        Contaings lists with start and end dates
     fuel_type : int
         Fuel type
+    date_list : list
+        Contaings lists with start and end dates
     bd_app_elec : list
         Base demand applications (electricity)
     reg_lu : list
@@ -235,7 +235,7 @@ def create_timesteps_app(date_list, fuel_type, bd_app_elec, reg_lu, fuel_type_lu
 
     return data_timesteps_elec
 
-def create_timesteps_hd(date_list, fuel_type, bd_hd_gas, reg_lu, fuel_type_lu, timestep_dates): # TODO: HIER GIBTS NOCH ERROR
+def create_timesteps_hd(fuel_type, date_list, bd_hd_gas, reg_lu, fuel_type_lu, timestep_dates): # TODO: HIER GIBTS NOCH ERROR
     '''
     This function creates the simulation time steps for which the heating energy is calculated.
     Then it selects energy demand from the yearl list for the simulation period.
@@ -627,27 +627,28 @@ def get_bd_hd_gas(shape_hd_gas, reg_lu, fuel_type_lu, fuel_bd_data):
     return fuel_type_per_region_hourly
 
 def conversion_ktoe_gwh(data_ktoe):
-    """ Conversion of ktoe to gwh according to
-    https://www.iea.org/statistics/resources/unitconverter/
-
-    Arguments
-    =========
-    -data_ktoe  [float] Energy demand in ktoe
+    """Conversion of ktoe to gwh
+    
+    Parameters
+    ----------
+    data_ktoe : float
+        Energy demand in ktoe
 
     Returns
-    =========
-    -data_gwh   [float] Energy demand in GWh
+    -------
+    data_gwh : float
+        Energy demand in GWh
+
+    Notes
+    -----
+    https://www.iea.org/statistics/resources/unitconverter/
     """
 
     data_gwh = data_ktoe * 11.6300000
 
     return data_gwh
 
 
-# ------------------------- New Code
-
-
-
 def timesteps_full_year():
     '''Creates list with every date of the base year
 
@@ -898,3 +899,17 @@ def read_data(paths_dict):
     data['hourly_gas_shape'] = hourly_gas_shape
 
     return data
+
+    def write_YAML(yaml_write, path_YAML):
+        """ Creates a YAML file with the timesteps IDs
+        
+        l = [{'id': value, 'start': 'p', 'end': 'P2',   }
+        
+        """
+        if yaml_write: 
+            import yaml
+        _, yaml_list = mf.timesteps_full_year()                                 # Create timesteps for full year (wrapper-timesteps)
+
+        with open(path_YAML, 'w') as outfile:
+            yaml.dump(yaml_list, outfile, default_flow_style=False)
+        return