fix bug in thermal demand and water demand generator

in last version the generated demand for heating and hot water was python list, which is changed into pandas series with timestample. otherwise the Prediction would cause error

Prosumer/scripts/time_series_processing.py

@@ -135,19 +135,44 @@ def generate_profile(t_start, t_step, t_horizon, prediction='Perfect', **kwargs)
         elif profile == 'generate_therm_demand_tek':
             bld_typ = kwargs[profile][0]
             total_demand = kwargs[profile][1]
-            weather = kwargs[profile][2]
+            weather = kwargs[profile][2]['temperature'].values
             year = int(pd.Timestamp(t_start).year)
 
-            input_profiles['therm_demand'] = tek.gen_heat_profile(
-                building_typ=bld_typ, temperature_profile=weather, year=year,
+            demands = tek.gen_heat_profile(building_typ=bld_typ,
+                                           temperature_profile=weather,
+                                           year=year,
                                            yearly_demand=total_demand)
+
+            profile_date = pd.date_range(start=str(year)+"0101", periods=8760,
+                                         freq='H')
+            demands = pd.Series(demands, index=profile_date)
+
+            predictors['therm_demand'] = PredictionGenerator(demands,
+                                                             method=prediction)
+            input_profiles['therm_demand'] = predictors['therm_demand'].predict(
+                str(t_step) + 'H',
+                t_start,
+                t_horizon / t_step)
         elif profile == 'generate_water_demand_tek':
             bld_typ = kwargs[profile][0]
             total_demand = kwargs[profile][1]
             year = int(pd.Timestamp(t_start).year)
 
-            input_profiles['hot_water_demand'] = tek.gen_hot_water_profile(
+            # input_profiles['hot_water_demand'] = tek.gen_hot_water_profile(
+            #     building_typ=bld_typ, year=year, yearly_demand=total_demand)
+
+            demands = tek.gen_hot_water_profile(
                 building_typ=bld_typ, year=year, yearly_demand=total_demand)
+            profile_date = pd.date_range(start=str(year) + "0101", periods=8760,
+                                         freq='H')
+            demands = pd.Series(demands, index=profile_date)
+
+            predictors['hot_water_demand'] = PredictionGenerator(demands,
+                                                                 method=prediction)
+            input_profiles['hot_water_demand'] = predictors['hot_water_demand'].predict(
+                str(t_step) + 'H',
+                t_start,
+                t_horizon / t_step)
         else:
             # apply prediction methods upon other input profiles (air_temp, irradiance, etc.)
             predictors[profile] = PredictionGenerator(kwargs[profile], method=prediction)