add option for thermal demand generator tek and fix bug for show result

thermal demand generation with tek data for therm_demand and water_demand are added since Tooling scripts are modified

bug in show_results, if Storage size is equal to 0, the calculation for charge_status leads divide zero error. fix bug with additional judgment wheather the size is 0

Prosumer/main.py

@@ -141,13 +141,28 @@ class Main:
                         input_profiles[self.name_list]['demand_electricity'] = \
                             ts.generate_profile(self.t_begin_profile, self.t_step, self.t_profile_length,
                             generate_elec_demand=('h0', self.elec_demand_yearly))
-
                     elif path_item == 'demand_heat':
                         input_profiles[self.name_list]['demand_heat'] = \
                             ts.generate_profile(self.t_begin_profile, self.t_step, self.t_profile_length,
                             generate_therm_demand=('EFH', self.therm_demand_yearly, input_profiles[self.name_list]['temperature']),
                             generate_water_demand=('EFH', self.hot_water_demand_yearly, input_profiles[self.name_list]['temperature']))
-
+                    elif path_item == 'demand_heat_tek':
+                        input_profiles[self.name_list]['demand_heat'] = \
+                            ts.generate_profile(self.t_begin_profile,
+                                                self.t_step,
+                                                self.t_profile_length,
+                                                generate_therm_demand_tek=(
+                                                    'Verwaltungsgebäude',
+                                                    self.therm_demand_yearly,
+                                                    input_profiles[
+                                                        self.name_list][
+                                                        'temperature']),
+                                                generate_water_demand_tek=(
+                                                    'Verwaltungsgebäude',
+                                                    self.hot_water_demand_yearly,
+                                                    input_profiles[
+                                                        self.name_list][
+                                                        'temperature']))
             # Generate prediction time series
             # todo: nicer coding required. generic code for all input timeseries
             for path_item in prosumer_data_path_dict[self.name_list]:
@@ -163,7 +178,8 @@ class Main:
                         self.t_start_loop:self.t_start_loop + pd.Timedelta(hours=self.t_current_value_length - 1)]
                     # Add timseries to prosumer model
                     self.prosumer[self.name_list].add_profile(predict_electric_demand)
-                elif path_item == 'demand_heat':
+                elif path_item == 'demand_heat' or path_item == \
+                        'demand_heat_tek':
                     predict_heat_demand = ts.generate_profile(
                         self.t_start_loop, self.t_step, self.t_horizon,
                         therm_demand=input_profiles[self.name_list]['demand_heat']['therm_demand'],
@@ -374,7 +390,8 @@ class Main:
             for components in all_comp['Components']:
                 comp_object = all_comp['Components'][components]
                 comp_type = comp_object.get_type()
-                if comp_type == 'Storage':
+                if comp_type == 'Storage' and all_results.iloc[
+                    amount_results-1][('cap', components[0])] != 0:
                     self.charge_status[name_list][components[0]] = all_results.iloc[amount_results-1][('energy', components[0])]\
                                                         /all_results.iloc[amount_results-1][('cap', components[0])]
         except ValueError:

Prosumer/scripts/time_series_processing.py

@@ -29,6 +29,7 @@ from datetime import datetime
 from Tooling.predictor.PredictionGenerator import PredictionGenerator
 from Tooling.demand_generator.electrical_demand.ElectricalDemand import ElectricalDemand
 from Tooling.demand_generator.thermal_demand.ThermalDemand import ThermalDemand
+import Tooling.demand_generator.tek_data.gen_heat_profile as tek
 
 
 def generate_profile(t_start, t_step, t_horizon, prediction='Perfect', **kwargs):
@@ -135,10 +136,18 @@ def generate_profile(t_start, t_step, t_horizon, prediction='Perfect', **kwargs)
             bld_typ = kwargs[profile][0]
             total_demand = kwargs[profile][1]
             weather = kwargs[profile][2]
+            year = int(pd.Timestamp(t_start).year)
+
+            input_profiles['therm_demand'] = tek.gen_heat_profile(
+                building_typ=bld_typ, temperature_profile=weather, year=year,
+                yearly_demand=total_demand)
         elif profile == 'generate_water_demand_tek':
             bld_typ = kwargs[profile][0]
             total_demand = kwargs[profile][1]
-            weather = kwargs[profile][2]
+            year = int(pd.Timestamp(t_start).year)
+
+            input_profiles['hot_water_demand'] = tek.gen_hot_water_profile(
+                building_typ=bld_typ, year=year, yearly_demand=total_demand)
         else:
             # apply prediction methods upon other input profiles (air_temp, irradiance, etc.)
             predictors[profile] = PredictionGenerator(kwargs[profile], method=prediction)