Stats

• Problem statistics
• Profile generation
• Graph simplification
• Tests for the above

src/topupopt/data/buildings/dk/heat.py

@@ -4,7 +4,9 @@
 import numpy as np
 from geopandas import GeoDataFrame
 from ...misc.utils import discrete_sinusoid_matching_integral
-from ...misc.utils import create_profile_using_time_weighted_state
+# from ...misc.utils import create_profile_using_time_weighted_state
+from ...misc.utils import generate_manual_state_correlated_profile
+from ...misc.utils import generate_state_correlated_profile
 from .bbr import label_bbr_entrance_id, label_bbr_housing_area
 
 # *****************************************************************************
@@ -66,6 +68,8 @@ def heat_demand_dict_by_building_entrance(
     key_bbr_entr_id: str = label_bbr_entrance_id,
     avg_state: list = None,
     state_correlates_with_output: bool = False,
+    deviation_gain: float = 1.0,
+    solver: str = 'glpk'
 ) -> dict:
     
     # initialise dict for each building entrance
@@ -84,11 +88,8 @@ def heat_demand_dict_by_building_entrance(
         # for each building
         for building_index in building_indexes:
             # get relevant data
-            # base_load_avg_ratio = 0.3
-            # specific_demand = 107 # kWh/m2/year
             area = gdf_buildings.loc[building_index][label_bbr_housing_area]
-
-            # estimate its demand
+            # generate the profile
             if type(avg_state) == type(None):
                 # ignore states
                 heat_demand_profiles.append(
@@ -104,21 +105,49 @@ def heat_demand_dict_by_building_entrance(
                     )
                 )
 
-            else:
-                # states matter
+            elif type(deviation_gain) == type(None):
+                # states matter but the gain must be determined
+                # heat_demand_profiles.append(
+                #     np.array(
+                #         create_profile_using_time_weighted_state(
+                #             integration_result=(
+                #                 bdg_specific_demand[building_index] * area
+                #             ),
+                #             avg_state=avg_state,
+                #             time_interval_durations=time_interval_durations,
+                #             min_to_max_ratio=bdg_ratio_min_max[building_index],
+                #             state_correlates_with_output=state_correlates_with_output,
+                #         )
+                #     )
+                # )
                 heat_demand_profiles.append(
                     np.array(
-                        create_profile_using_time_weighted_state(
+                        generate_state_correlated_profile(
                             integration_result=(
                                 bdg_specific_demand[building_index] * area
-                            ),
-                            avg_state=avg_state,
+                            ), 
+                            states=avg_state, 
                             time_interval_durations=time_interval_durations,
-                            min_to_max_ratio=bdg_ratio_min_max[building_index],
-                            state_correlates_with_output=state_correlates_with_output,
+                            states_correlate_profile=state_correlates_with_output,
+                            min_max_ratio=bdg_ratio_min_max[building_index],
+                            solver=solver
+                            )
+                        )
+                    )
+            else:
+                # states matter and the gain is predefined
+                heat_demand_profiles.append(
+                    np.array(
+                        generate_manual_state_correlated_profile(
+                            integration_result=(
+                                bdg_specific_demand[building_index] * area
+                            ), 
+                            states=avg_state, 
+                            time_interval_durations=time_interval_durations, 
+                            deviation_gain=deviation_gain
+                            )
                         )
                     )
-                )
 
         # add the profiles, time step by time step
         if len(heat_demand_profiles) == 0: