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updating solar recommender
This commit is contained in:
Khalim Conn-Kowlessar
parent
53b2ca05b6
commit
d45059e40d
4 changed files with 73 additions and 90 deletions
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@ -8,6 +8,7 @@ import time
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from backend.app.db.functions.solar_functions import get_solar_data, store_batch_data
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from utils.logger import setup_logger
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from sklearn.preprocessing import MinMaxScaler
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from recommendations.Costs import Costs
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logger = setup_logger()
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@ -107,7 +108,14 @@ class GoogleSolarApi:
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@lru_cache(maxsize=128)
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def get(
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self, longitude, latitude, energy_consumption, required_quality="MEDIUM", is_building=False, session=None,
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self,
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longitude,
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latitude,
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energy_consumption,
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property_instance=None,
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required_quality="MEDIUM",
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is_building=False,
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session=None,
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uprn=None
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):
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"""
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@ -116,6 +124,7 @@ class GoogleSolarApi:
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:param longitude: The longitude of the location.
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:param latitude: The latitude of the location.
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:param energy_consumption: The energy consumption of the building/unit associated to the longitude and latitude.
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:param property_instance: The property instance associated to the longitude and latitude.
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:param required_quality: The required quality of the data (default is "MEDIUM").
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:param is_building: Whether the energy consumption is for a building or a unit.
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:param session: The database session to use for the query (default is None).
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@ -158,7 +167,9 @@ class GoogleSolarApi:
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self.roof_segment_indexes = [segment['segmentIndex'] for segment in self.roof_segments]
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# We now start finding the solar panel configurations
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self.optimise_solar_configuration(energy_consumption=energy_consumption, is_building=is_building)
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self.optimise_solar_configuration(
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energy_consumption=energy_consumption, is_building=is_building, property_instance=property_instance
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)
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def save_to_db(self, session, uprns_to_location, scenario_type):
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if self.insights_data is None:
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@ -178,7 +189,7 @@ class GoogleSolarApi:
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"yearly_dc_energy",
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"total_cost",
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"panneled_roof_area",
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"array_warrage",
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"array_wattage",
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"initial_ac_kwh_per_year",
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"lifetime_ac_kwh",
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"roi",
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@ -191,7 +202,7 @@ class GoogleSolarApi:
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"yearly_dc_energy": "yearly_dc_kwh",
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"total_cost": "cost",
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"panneled_roof_area": "panelled_roof_area",
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"array_warrage": "array_kwhp",
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"array_wattage": "array_kwhp",
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"initial_ac_kwh_per_year": "yearly_ac_kwh",
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}
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)
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@ -226,12 +237,14 @@ class GoogleSolarApi:
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installation_life_span)) /
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(1 - efficiency_depreciation_factor))
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def optimise_solar_configuration(self, energy_consumption, is_building=False):
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def optimise_solar_configuration(self, energy_consumption, is_building=False, property_instance=None):
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"""
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Optimise the solar panel configuration for the building.
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:return:
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"""
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cost_instance = Costs(property_instance=property_instance) if property_instance is not None else None
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# Remove any north facing roof segments
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panel_performance = []
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for config in self.insights_data["solarPotential"]["solarPanelConfigs"]:
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@ -246,7 +259,14 @@ class GoogleSolarApi:
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wattage = segment["panelsCount"] * self.insights_data["solarPotential"]["panelCapacityWatts"]
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generated_dc_energy = segment["yearlyEnergyDcKwh"]
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ratio = generated_dc_energy / wattage
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cost = MCS_SOLAR_PV_COST_DATA["average_cost_per_kwh"] * (wattage / 1000)
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if cost_instance is None:
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cost = MCS_SOLAR_PV_COST_DATA["average_cost_per_kwh"] * (wattage / 1000)
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else:
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cost = cost_instance.solar_pv(
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wattage=wattage, has_battery=False
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)["total"]
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roi_summary.append(
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{
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"segmentIndex": segment["segmentIndex"],
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@ -274,7 +294,7 @@ class GoogleSolarApi:
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"total_cost": total_cost,
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"weighted_ratio": weighted_ratio,
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"panneled_roof_area": roi_summary["panneled_roof_area"].sum(),
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"array_warrage": roi_summary["n_panels"].sum() * self.panel_wattage
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"array_wattage": roi_summary["n_panels"].sum() * self.panel_wattage
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}
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)
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@ -290,7 +310,7 @@ class GoogleSolarApi:
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# Remove anything where the total ac energy is less than half of the array wattage
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panel_performance = panel_performance[
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(panel_performance["initial_ac_kwh_per_year"] / panel_performance["array_warrage"]) >= 0.5
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(panel_performance["initial_ac_kwh_per_year"] / panel_performance["array_wattage"]) >= 0.5
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]
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# 2) Calculate the liftime solar energy production
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@ -546,7 +546,8 @@ async def trigger_plan(body: PlanTriggerRequest):
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energy_consumption=unit["energy_consumption"],
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is_building=False,
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session=session,
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uprn=unit["uprn"]
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uprn=unit["uprn"],
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property_instance=property_instance
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)
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# Store the data in the database
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@ -100,7 +100,7 @@ class SolarPvRecommendations:
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roof_coverage_percent = round(recommendation_config["panneled_roof_area"] / total_roof_area * 100)
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# Spread the cost to the individual units - adding a 20% contingency
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total_cost = recommendation_config["total_cost"] / n_units
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kw = np.floor(recommendation_config["array_warrage"] / 100) / 10
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kw = np.floor(recommendation_config["array_wattage"] / 100) / 10
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# Default to a weeks work for a team of 3 people doing 8 hour days
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labour_days = 5
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labour_hours = 3 * 8 * labour_days
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@ -150,84 +150,46 @@ class SolarPvRecommendations:
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self.recommend_building_analysis(phase)
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return
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solar_pv_percentage = self.property.solar_pv_percentage
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# We round up to the neaest 10%
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solar_pv_percentage = np.ceil(solar_pv_percentage * 10) / 10
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panel_performance = self.property.solar_panel_configuration["panel_performance"]
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roof_area = self.property.roof_area
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# For the solar recommendations, we produce the following scenarios:
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# 1) Solar panels only, we present a high, medium and low coverage
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# 2) With and without battery
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roof_coverage_scenarios = [
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solar_pv_percentage - 0.1, solar_pv_percentage,
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]
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if solar_pv_percentage <= 0.4:
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roof_coverage_scenarios.append(solar_pv_percentage + 0.1)
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# We make sure we haven't gone too low or high - we allow no more than 60% coverage
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roof_coverage_scenarios = [v for v in roof_coverage_scenarios if 0 <= v <= 0.6]
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# If we only have two scenarios, we add a coverage scenario 10% less than the smallest
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if len(roof_coverage_scenarios) == 2:
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roof_coverage_scenarios.insert(0, roof_coverage_scenarios[0] - 0.1)
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battery_scenarios = [False, True]
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solar_configurations = panel_performance.head(3).reset_index(drop=True)
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scenarios_with_wattage = []
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for roof_coverage in roof_coverage_scenarios:
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# We now have a property which is potentially suitable for solar PV
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solar_pv_roof_area = self.property.get_solar_pv_roof_area(roof_coverage)
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# We combine each of these configurations with estimates with and without a battery
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for rank, recommendation_config in solar_configurations.iterrows():
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roof_coverage_percent = round(recommendation_config["panneled_roof_area"] / roof_area * 100)
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for has_battery in [False, True]:
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cost_result = self.costs.solar_pv(
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wattage=recommendation_config["array_wattage"], has_battery=has_battery
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)
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kw = np.floor(recommendation_config["array_wattage"] / 100) / 10
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if has_battery:
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description = (f"Install a {kw} kilowatt-peak (kWp) solar photovoltaic (PV) panel system on "
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f"{round(roof_coverage_percent)}% the roof, with a battery storage system.")
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else:
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description = (f"Install a {kw} kilowatt-peak (kWp) solar photovoltaic (PV) p"
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f"anel system on {round(roof_coverage_percent)}% the roof.")
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number_solar_panels = np.floor(solar_pv_roof_area / self.SOLAR_PANEL_AREA)
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solar_panel_wattage = number_solar_panels * self.SOLAR_PANEL_WATTAGE
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already_installed = "solar_pv" in self.property.already_installed
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if already_installed:
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cost_result = override_costs(cost_result)
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if solar_panel_wattage < self.MIN_SYSTEM_WATTAGE:
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continue
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solar_panel_wattage = np.clip(
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a=solar_panel_wattage, a_min=self.MIN_SYSTEM_WATTAGE, a_max=self.MAX_SYSTEM_WATTAGE
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)
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scenarios_with_wattage.append((roof_coverage, solar_panel_wattage))
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# We trim the scenarios, so that we don't have duplicate wattages
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scenarios_with_wattage = self.trim_solar_wattage_options(scenarios_with_wattage)
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# Produce the cross product of the scenarios
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scenarios = [
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(roof, wattage, battery) for roof, wattage in scenarios_with_wattage for battery in battery_scenarios
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]
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# We deduce the wattage of the solar panels based on the roof coverage
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for roof_coverage, solar_panel_wattage, has_battery in scenarios:
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# We now have a property which is potentially suitable for solar PV
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roof_coverage_percent = round(roof_coverage * 100)
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# Given the wattage, we estimate the cost of the solar PV system. This is based on the MCS database
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# of solar PV installations
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cost_result = self.costs.solar_pv(wattage=solar_panel_wattage, has_battery=has_battery)
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kw = np.floor(solar_panel_wattage / 100) / 10
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if has_battery:
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description = (f"Install a {kw} kilowatt-peak (kWp) solar photovoltaic (PV) panel system on "
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f"{round(roof_coverage_percent)}% the roof, with a battery storage system.")
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else:
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description = (f"Install a {kw} kilowatt-peak (kWp) solar photovoltaic (PV) p"
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f"anel system on {round(roof_coverage_percent)}% the roof.")
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already_installed = "solar_pv" in self.property.already_installed
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if already_installed:
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cost_result = override_costs(cost_result)
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self.recommendation.append(
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{
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"phase": phase,
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"parts": [],
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"type": "solar_pv",
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"description": description,
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"starting_u_value": None,
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"new_u_value": None,
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"sap_points": None,
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"already_installed": already_installed,
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**cost_result,
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# This is required for simulating the SAP impact. solar_pv_percentage is between 0 & 1 so we scale
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# back up here
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"photo_supply": 100 * roof_coverage,
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"has_battery": has_battery,
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"description_simulation": {"photo-supply": 100 * roof_coverage},
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}
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)
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self.recommendation.append(
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{
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"phase": phase,
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"parts": [],
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"type": "solar_pv",
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"description": description,
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"starting_u_value": None,
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"new_u_value": None,
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"sap_points": None,
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"already_installed": already_installed,
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**cost_result,
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# This is required for simulating the SAP impact. solar_pv_percentage is between 0 & 1 so we
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# scale
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# back up here
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"photo_supply": roof_coverage_percent,
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"has_battery": has_battery,
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"description_simulation": {"photo-supply": roof_coverage_percent},
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}
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)
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@ -53,14 +53,14 @@ class WindowsRecommendations:
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if not number_of_windows:
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raise ValueError("Number of windows not specified")
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if windows_area is not None:
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raise Exception("We have windows area, we should use this data for our recommendations!!!")
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if self.property.windows["has_glazing"] & (
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self.property.windows["glazing_coverage"] == "full"
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):
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return
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if windows_area is not None:
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raise Exception("We have windows area, we should use this data for our recommendations!!!")
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# We scale the number of windows based on the proportion of existing glazing
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if self.property.data["multi-glaze-proportion"] != "":
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n_windows_scalar = 1 - (
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