from backend.Property import Property from typing import List from itertools import groupby from recommendations.FloorRecommendations import FloorRecommendations from recommendations.WallRecommendations import WallRecommendations from recommendations.RoofRecommendations import RoofRecommendations from recommendations.VentilationRecommendations import VentilationRecommendations from recommendations.FireplaceRecommendations import FireplaceRecommendations from recommendations.LightingRecommendations import LightingRecommendations from recommendations.SolarPvRecommendations import SolarPvRecommendations from recommendations.WindowsRecommendations import WindowsRecommendations from recommendations.HeatingRecommender import HeatingRecommender from recommendations.HotwaterRecommendations import HotwaterRecommendations from backend.ml_models.AnnualBillSavings import AnnualBillSavings class Recommendations: """ High level recommendations class, which sits above the measure specific recommendation classes """ def __init__( self, property_instance: Property, materials: List, exclusions: List[str] = None, ): """ :param property_instance: Instance of the Property class, for the home associated to property_id :param materials: List of materials to be used in the recommendations """ self.property_instance = property_instance self.materials = materials self.exclusions = exclusions if exclusions else [] self.floor_recommender = FloorRecommendations(property_instance=property_instance, materials=materials) self.wall_recomender = WallRecommendations(property_instance=property_instance, materials=materials) self.roof_recommender = RoofRecommendations(property_instance=property_instance, materials=materials) self.ventilation_recomender = VentilationRecommendations( property_instance=property_instance, materials=materials ) self.fireplace_recommender = FireplaceRecommendations(property_instance=property_instance) self.lighting_recommender = LightingRecommendations(property_instance=property_instance, materials=materials) self.windows_recommender = WindowsRecommendations(property_instance=property_instance, materials=materials) self.solar_recommender = SolarPvRecommendations(property_instance=property_instance) self.heating_recommender = HeatingRecommender(property_instance=property_instance) self.hotwater_recommender = HotwaterRecommendations(property_instance=property_instance) def recommend(self): """ This method runs the recommendations for the individual measures and then appends them to a list for output The recommendations are implemented in order of suggested phase, from fabric first to heating systems, to renewables. :return: """ property_recommendations = [] phase = 0 # Building Fabric if "wall_insulation" not in self.exclusions: self.wall_recomender.recommend(phase=phase) if self.wall_recomender.recommendations: property_recommendations.append(self.wall_recomender.recommendations) phase += 1 if "roof_insulation" not in self.exclusions: self.roof_recommender.recommend(phase=phase) if self.roof_recommender.recommendations: property_recommendations.append(self.roof_recommender.recommendations) phase += 1 # Ventilation recommendations # We only produce a ventilation recommendation if the property is recommended to have wall or roof # insulation # We will not attribute a SAP impact to the ventilation recommendation, since we've seen that this # has no # real impact on the SAP score. Therefore, we don't need to include phasing for ventilation. If we # have any # wall or roof recommendations, we will ensure that ventilation is included in the simulation if "ventilation" not in self.exclusions: if self.wall_recomender.recommendations or self.roof_recommender.recommendations: self.ventilation_recomender.recommend() if self.ventilation_recomender.recommendation: property_recommendations.append(self.ventilation_recomender.recommendation) if "floor_insulation" not in self.exclusions: self.floor_recommender.recommend(phase=phase) if self.floor_recommender.recommendations: property_recommendations.append(self.floor_recommender.recommendations) phase += 1 if "windows" not in self.exclusions: self.windows_recommender.recommend(phase=phase) if self.windows_recommender.recommendation: property_recommendations.append(self.windows_recommender.recommendation) phase += 1 if "fireplace" not in self.exclusions: self.fireplace_recommender.recommend(phase=phase) if self.fireplace_recommender.recommendation: property_recommendations.append(self.fireplace_recommender.recommendation) phase += 1 # Heating and Electical systems if "heating" not in self.exclusions: self.heating_recommender.recommend(phase=phase) if self.heating_recommender.recommendations: property_recommendations.append(self.heating_recommender.recommendations) # We check if we have distinct heating and heating controls recommendations # If so, we increment by 2 (one of the heating system, one for the heating controls) # otherwise we incremenet by 1 max_used_phase = max([rec["phase"] for rec in self.heating_recommender.recommendations]) amount_to_increment = max_used_phase - phase + 1 phase += amount_to_increment # Hot water if "hot_water" not in self.exclusions: self.hotwater_recommender.recommend(phase=phase) if self.hotwater_recommender.recommendations: property_recommendations.append(self.hotwater_recommender.recommendations) phase += 1 if "lighting" not in self.exclusions: self.lighting_recommender.recommend(phase=phase) if self.lighting_recommender.recommendation: property_recommendations.append(self.lighting_recommender.recommendation) phase += 1 # Renewables if "solar_pv" not in self.exclusions: self.solar_recommender.recommend(phase=phase) if self.solar_recommender.recommendation: property_recommendations.append(self.solar_recommender.recommendation) phase += 1 # We insert temporary ids into the recommendations which is important for the optimiser later property_recommendations = self.insert_temp_recommendation_id(property_recommendations) # We also need to create the representative recommendations for each recommendation type property_representative_recommendations = self.create_representative_recommendations(property_recommendations) return property_recommendations, property_representative_recommendations @staticmethod def create_representative_recommendations(property_recommendations): """ This method will create a representative recommendation for each recommendation type In order to create a representative recommendation, we choose the recommendation that has: 1) Where a U-value is available, has the best U-value to cost ratio 2) Where SAP points are available, has the best SAP points to cost ratio We don't include mechanical ventilation in the representative recommendations, since we don't attribute a SAP impact to this recommendation :return: """ property_representative_recommendations = [] for recommendations_by_type in property_recommendations: if recommendations_by_type[0].get("type") == "mechanical_ventilation": continue has_u_value = recommendations_by_type[0].get("new_u_value") is not None has_sap_points = recommendations_by_type[0].get("sap_points") is not None # When check if these recommendations have two different types, such as solid wall insulation # If we have multiple types, we group by type and then select the best recommendation for each type recommendations_by_type = sorted(recommendations_by_type, key=lambda x: x["type"]) representative_recommendations = [] for type, recommendations in groupby(recommendations_by_type, key=lambda x: x["type"]): recommendations = list(recommendations) # We also create an efficiency key, which is used to sort the recommendations if has_u_value: # We sort by the cost per U-value improvement - the lower the better for rec in recommendations: rec["efficiency"] = rec["total"] / rec["starting_u_value"] - rec["new_u_value"] elif not has_u_value and has_sap_points: # Sort the options by the cost per SAP point improvement - the lower the better for rec in recommendations: rec["efficiency"] = rec["total"] / rec["sap_points"] else: # Sort the options by cost - the lower the better for rec in recommendations: rec["efficiency"] = rec["total"] recommendations.sort( key=lambda x: x["efficiency"] ) representative_recommendations.append(recommendations[0]) property_representative_recommendations.extend(representative_recommendations) return property_representative_recommendations @staticmethod def insert_temp_recommendation_id(property_recommendations): """ Creates a temporary recommendation id which is needed for filtering recommendations between default and no, after the optimiser has been run :param property_recommendations: nested list of recommendations, grouped by data_types :return: Updated recommendations_to_upload, where where recommendation has a "recommendation_id" integer inserted """ idx = 0 for recs in property_recommendations: for rec in recs: rec["recommendation_id"] = f"{str(idx)}_phase={str(rec['phase'])}" idx += 1 return property_recommendations @classmethod def calculate_recommendation_impact(cls, property_instance, all_predictions, recommendations): """ Given predictions from the model apis, with method will update the recommendations with the predicted impact of the recommendation on the property :param property_instance: Instance of the Property class, for the home associated to property_id :param all_predictions: dictionary of predictions from the model apis :param recommendations: dictionary of recommendations for the property :return: """ property_sap_predictions = all_predictions["sap_change_predictions"][ all_predictions["sap_change_predictions"]["property_id"] == str(property_instance.id) ] property_heat_predictions = all_predictions["heat_demand_predictions"][ all_predictions["heat_demand_predictions"]["property_id"] == str(property_instance.id) ] property_carbon_predictions = all_predictions["carbon_change_predictions"][ all_predictions["carbon_change_predictions"]["property_id"] == str(property_instance.id) ] property_recommendations = recommendations[property_instance.id].copy() # We calculate the impact by phase sap_phase_impact = property_sap_predictions.groupby("phase")["predictions"].median().reset_index() heat_phase_impact = property_heat_predictions.groupby("phase")["predictions"].median().reset_index() carbon_phase_impact = property_carbon_predictions.groupby("phase")["predictions"].median().reset_index() # The heat demand change is the difference between the starting heat demand and the value at the final phase expected_heat_demand = property_instance.floor_area * ( heat_phase_impact[heat_phase_impact["phase"] == max(heat_phase_impact["phase"])]["predictions"].values[0] ) starting_heat_demand = ( float(property_instance.data["energy-consumption-current"]) * property_instance.floor_area ) # This is the unadjusted resulting heat demand predicted_heat_demand_change = starting_heat_demand - expected_heat_demand # We don't want to adjust the heat demand for mechanical ventilation so we add it back on # We adjust the heat demand figures to align to the UCL paper current_adjusted_energy = AnnualBillSavings.adjust_energy_to_metered( epc_energy_consumption=starting_heat_demand, current_epc_rating=property_instance.data["current-energy-rating"], ) expected_adjusted_energy = AnnualBillSavings.adjust_energy_to_metered( epc_energy_consumption=expected_heat_demand, current_epc_rating=property_instance.data["current-energy-rating"], ) adjusted_heat_demand_change = ( current_adjusted_energy - expected_adjusted_energy ) for recommendations_by_type in property_recommendations: for rec in recommendations_by_type: if rec["type"] == "mechanical_ventilation": # We don't have a percieved sap impact of mechanical ventilation continue new_heat_demand = property_heat_predictions[property_heat_predictions["recommendation_id"] == str( rec["recommendation_id"] )]["predictions"].values[0] new_carbon = property_carbon_predictions[property_carbon_predictions["recommendation_id"] == str( rec["recommendation_id"] )]["predictions"].values[0] new_sap = property_sap_predictions[property_sap_predictions["recommendation_id"] == str( rec["recommendation_id"] )]["predictions"].values[0] if rec["phase"] == 0: predicted_sap_points = new_sap - float(property_instance.data["current-energy-efficiency"]) predicted_co2_savings = float(property_instance.data["co2-emissions-current"]) - new_carbon predicted_heat_demand = property_instance.floor_area * ( float(property_instance.data["energy-consumption-current"]) - new_heat_demand ) else: previous_phase = rec["phase"] - 1 predicted_sap_points = ( new_sap - sap_phase_impact[sap_phase_impact["phase"] == previous_phase]["predictions"].values[0] ) predicted_co2_savings = ( carbon_phase_impact[carbon_phase_impact["phase"] == previous_phase]["predictions"].values[0] - new_carbon ) predicted_heat_demand = property_instance.floor_area * ( heat_phase_impact[heat_phase_impact["phase"] == previous_phase]["predictions"].values[0] - new_heat_demand ) if rec["type"] == "low_energy_lighting": # For the moment, we cap the number of SAP points that can be achieved by ventilation at 2 rec["sap_points"] = min(predicted_sap_points, LightingRecommendations.SAP_LIMIT) rec["co2_equivalent_savings"] = min(predicted_co2_savings, rec["co2_equivalent_savings"]) rec["heat_demand"] = min(predicted_heat_demand, rec["heat_demand"]) else: rec["sap_points"] = predicted_sap_points rec["co2_equivalent_savings"] = predicted_co2_savings rec["heat_demand"] = predicted_heat_demand # Round to 2 decimal places rec["sap_points"] = round(rec["sap_points"], 2) # We now calculate the adjusted heat demand for this recommendation, which is simply the percentage # of the total adjusted heat demand change. The percentage we use is this recommendation's percentage # of the total heat demand per square meter change rec["adjusted_heat_demand"] = adjusted_heat_demand_change * ( rec["heat_demand"] / predicted_heat_demand_change ) # We make sure this is NOT below 0 rec["adjusted_heat_demand"] = max(0, rec["adjusted_heat_demand"]) # Depending on the property's tarriff, we calculate the amount of energy savings this measure will bring if property_instance.energy_source == "electricity": rec["energy_cost_savings"] = AnnualBillSavings.estimate_electric(rec["adjusted_heat_demand"]) elif property_instance.energy_source == "electricity_and_gas": rec["energy_cost_savings"] = AnnualBillSavings.estimate(rec["adjusted_heat_demand"]) else: raise ValueError("Invalid value for energy source") if (rec["sap_points"] is None) and (rec["co2_equivalent_savings"] is None) or ( rec["heat_demand"] is None) or (rec["energy_cost_savings"] is None): raise ValueError("sap points, co2 or heat demand is missing") return property_recommendations, current_adjusted_energy, expected_adjusted_energy