import numpy as np import pandas as pd import backend.app.assumptions as assumptions from recommendations.Costs import Costs from recommendations.recommendation_utils import override_costs, estimate_pitched_roof_area class SolarPvRecommendations: # For domestic properties, we don't recommend a solar PV system with wattage outside of these # bounds MAX_SYSTEM_WATTAGE = 6000 MIN_SYSTEM_WATTAGE = 1000 # the maximum area of root we allow to be covered in solar panels for our recommendations. MAX_ROOF_AREA_PERCENTAGE = 0.7 SAP_POINTS_PER_5_PERCENT_ROOF_COVERAGE = 1 BACKUP_PANEL_PERFORMANCE = pd.DataFrame( [ { "n_panels": 4, "array_wattage": 1600, "initial_ac_kwh_per_year": assumptions.MEDIAN_WATTAGE_TO_AC * 1600, "panneled_roof_area": 4 * assumptions.RDSAP_AREA_PER_PANEL }, { "n_panels": 8, "array_warrage": 3200, "initial_ac_kwh_per_year": assumptions.MEDIAN_WATTAGE_TO_AC * 3200, "panneled_roof_area": 8 * assumptions.RDSAP_AREA_PER_PANEL }, ] ) PANEL_SIZES = [400, 435, 440, 445] def __init__(self, property_instance, materials: list): """ :param property_instance: Instance of the Property class, for the home associated to property_id """ self.property = property_instance self.costs = Costs(self.property) self.recommendation = [] self.panels_products = [ material for material in materials if material["type"] == "solar_pv" ] self.scaffolding_options = [ material for material in materials if material["type"] == "scaffolding" ] @staticmethod def trim_solar_wattage_options(scenarios_with_wattage): # Initialize the list with the first element, assuming the list is not empty trimmed_list = [scenarios_with_wattage[0]] # Iterate over the list starting from the second element for scenario in scenarios_with_wattage[1:]: # Compare the second element (index 1) of the current tuple with the last tuple in the trimmed list if scenario[1] > trimmed_list[-1][1]: trimmed_list.append(scenario) return trimmed_list def recommend_building_analysis(self, phase): """ This recommendation approach handles the case of producing solar PV recommendations at the building level, across multiple flats. For these recommendations, we don't include the battery option since it's impractical from a space perspective. :return: """ panel_performance = self.property.solar_panel_configuration["panel_performance"] total_roof_area = ( self.property.solar_panel_configuration["insights_data"]["solarPotential"]["wholeRoofStats"]["areaMeters2"] ) n_units = self.property.solar_panel_configuration["n_units"] # At a building level, we take a single configuration so that all properties a guaranteed to use # the same configuration best_configurations = panel_performance.head(1).reset_index(drop=True) for rank, recommendation_config in best_configurations.iterrows(): # If we dont have the panneled_roof_area in the recommendation_config we calculate it if recommendation_config.get("panneled_roof_area", None): # We spread the coverage across the individual units roof_coverage_percent = round( ((recommendation_config["panneled_roof_area"] / total_roof_area) * 100) / n_units ) else: raise Exception("IMPLEMENT ME") # We get solar PV options solar_product = [x for x in self.panels_products if x["id"] == recommendation_config["solar_product_id"]] if not solar_product: raise NotImplementedError( f"Solar product with id {recommendation_config['solar_product_id']} not found in " "panels_products" ) solar_product = solar_product[0] n_floors = ( self.property.number_of_storeys["number_of_storeys"] if self.property.number_of_storeys["number_of_storeys"] is not None else 3 ) total_cost = self.costs.solar_pv( solar_product=solar_product, scaffolding_options=self.scaffolding_options, n_floors=n_floors, )["total"] kw = np.floor(recommendation_config["array_wattage"] / 100) / 10 # Default to a weeks work for a team of 3 people doing 8 hour days labour_days = 5 labour_hours = 3 * 8 * labour_days description = (f"Install a {kw} kilowatt-peak (kWp) solar photovoltaic (PV) panel system on the roof " "of the building") initial_ac_kwh_per_year = recommendation_config["initial_ac_kwh_per_year"] self.recommendation.append( { "phase": phase, "parts": [], "type": "solar_pv", "measure_type": "solar_pv", "description": description, "starting_u_value": None, "new_u_value": None, "sap_points": None, "already_installed": False, "total": total_cost, "labour_days": labour_days, "labour_hours": labour_hours, # This is required for simulating the SAP impact. solar_pv_percentage is between 0 & 1 so we scale # back up here "photo_supply": roof_coverage_percent, "has_battery": False, "simulation_config": { "photo_supply_ending": roof_coverage_percent }, "initial_ac_kwh_per_year": initial_ac_kwh_per_year, "description_simulation": {"photo-supply": roof_coverage_percent}, "rank": rank, # Rank is used to get the representative recommendation - rank 0 will be chosen "innovation_rate": solar_product["innovation_rate"], } ) def _get_available_products(self, n_panels): """ Utility function to get the available solar PV products based on the number of panels :param n_panels: :return: """ available_products = [] for panel_size in self.PANEL_SIZES: system_size = (n_panels * panel_size) / 1000 prods = [ x for x in self.panels_products if abs(x["size"] - system_size) < 0.01 ] for x in prods: x["panel_size"] = panel_size available_products.extend(prods) return available_products def recommend(self, phase): """ We check if a property is potentially suitable for solar PV based on the following criteria: - The property is a house or bungalow - The property has a flat or pitched roof - The property does not have existing solar pv :return: """ if not self.property.is_solar_pv_valid(): return # If we have a buiilding level analysis, we implement separate logic if self.property.building_id is not None: self.recommend_building_analysis(phase) return non_invasive_recommendation = next( (r for r in self.property.non_invasive_recommendations if r["type"] == "solar_pv"), {"suitable": True} ) # We allow for the non-invasive recommendation to be that solar PV is not suitable if not non_invasive_recommendation["suitable"]: return if non_invasive_recommendation.get("array_wattage") is not None: if self.property.roof["is_flat"]: roof_area = self.property.insulation_floor_area else: roof_area = estimate_pitched_roof_area(floor_area=self.property.insulation_floor_area, ) solar_configurations = pd.DataFrame( [ { "array_wattage": non_invasive_recommendation["array_wattage"], "initial_ac_kwh_per_year": non_invasive_recommendation["initial_ac_kwh_per_year"], "panneled_roof_area": non_invasive_recommendation["panneled_roof_area"] } ] ) else: # TODO: There may be some instances where we don't want to use the solar API so we should cover for them panel_performance = self.property.solar_panel_configuration["panel_performance"].copy() # We don't allow for more than 70% of the roof to be covered panel_performance = panel_performance[ panel_performance["panneled_roof_area"] / self.property.roof_area <= self.MAX_ROOF_AREA_PERCENTAGE ] roof_area = self.property.roof_area solar_configurations = panel_performance.head(6).reset_index(drop=True) # We combine each of these configurations with estimates with and without a battery for rank, recommendation_config in solar_configurations.iterrows(): n_panels = recommendation_config["n_panels"] available_products = self._get_available_products(n_panels) # Given the available products in the database, we product the possible array of recommendations for solar_pv_product in available_products: # we take the paneled roof area and this tells us the roof coverage, based on 400W panels # We then look at the equivalent for larger panels, which will produce more energy in the same area paneled_roof_area = recommendation_config["panneled_roof_area"] roof_coverage_percent = round( ((paneled_roof_area / 400) * solar_pv_product["panel_size"]) / roof_area * 100 ) # We round up to the nearest 5 roof_coverage_percent = np.ceil(roof_coverage_percent / 5) * 5 # Note roof_coverage_percent is based on 400 watt panels, so we need to scale it up based on # largest panels that will produce more energy in the same area # Typically, we've observed that every 5% of additional roof coverage will result in at least # an additional 1 SAP points (though often 2 points) Given this, we can add a reasonable minimum # for the number of SAP points we might expect. We've observed that for some cases where properties # are hitting the higher SAP scores (e.g. EPC A and above), the model can sometimes under-predict # the number of SAP points. This appears to be due to a relatively small number of properties # actually achieving the upper echelons of EPC rating. This can be the case if we're simulating a # whole house retrofit where the home is getting complete insulation, a heat pump and solar panels. # Because panels are the final recommendation, they are often the measure that takes the home # into the medium to high EPC A ranges and so because of a lack of training data, this means that # we might sometime under-predict. This minimum is intended to try and reduce the negative impact # of this. This minimum is used in Recommendations.calculate_recommendation_impact minimum_sap_points = (roof_coverage_percent / 5) * self.SAP_POINTS_PER_5_PERCENT_ROOF_COVERAGE cost_result = self.costs.solar_pv( solar_product=solar_pv_product, scaffolding_options=self.scaffolding_options, n_floors=self.property.number_of_floors ) description = f"{solar_pv_product['description']} - {solar_pv_product['size']} kWp system" if self.property.in_conservation_area: description += " Property is in a consevation area - please check with local planning authority." already_installed = "solar_pv" in self.property.already_installed if already_installed: cost_result = override_costs(cost_result) self.recommendation.append( { "phase": phase, "parts": [solar_pv_product], "type": "solar_pv", "measure_type": "solar_pv", "description": description, "starting_u_value": None, "new_u_value": None, "sap_points": minimum_sap_points, "already_installed": already_installed, **cost_result, "has_battery": solar_pv_product["includes_battery"], "simulation_config": { "photo_supply_ending": roof_coverage_percent }, "initial_ac_kwh_per_year": recommendation_config["initial_ac_kwh_per_year"], "description_simulation": {"photo-supply": roof_coverage_percent}, "innovation_rate": solar_pv_product["innovation_rate"], } )