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https://github.com/Hestia-Homes/Model.git
synced 2026-06-08 11:17:27 +00:00
added some basic level of override so we force solar recommendations if we have inspections
This commit is contained in:
Khalim Conn-Kowlessar
parent
23eb26527c
commit
76716f35d3
3 changed files with 72 additions and 11 deletions
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@ -479,9 +479,7 @@ class GoogleSolarApi:
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roi_results = pd.DataFrame(roi_results)
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panel_performance = panel_performance.merge(
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roi_results, how="left", on="n_panels"
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)
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panel_performance = panel_performance.merge(roi_results, how="left", on="n_panels")
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# We want max roi, minimal generation deficit, and max generation value - we create a ranking score
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# Assign equal weights to each metric
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@ -742,7 +740,7 @@ class GoogleSolarApi:
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@classmethod
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def building_solar_analysis(
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cls, building_solar_config: List, input_properties: List[Property], session, google_solar_api_key: str,
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solar_materials: list
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solar_materials: list,
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):
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"""
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Perform the solar analysis for the building level
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@ -826,9 +824,21 @@ class GoogleSolarApi:
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@classmethod
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def unit_solar_analysis(
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cls, unit_solar_config: List, input_properties: List[Property], session, body, google_solar_api_key: str,
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solar_materials: list
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solar_materials: list, inspections_map: dict
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):
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"""
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Perform the solar analysis for the unit level
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:param unit_solar_config: List of unit solar configurations
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:param input_properties: List of properties
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:param session: Database session
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:param body: PlanTriggerRequest instance
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:param google_solar_api_key: Google Solar API key
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:param solar_materials: List of solar materials
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:param inspections_map: Dictionary mapping property IDs to inspection data
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:return:
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"""
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if not unit_solar_config:
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return input_properties
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@ -879,6 +889,15 @@ class GoogleSolarApi:
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property_instance=property_instance,
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)
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property_inspections = inspections_map.get(property_instance.id, {})
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if property_inspections:
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# If we have some inspections data, we check if we have some data which indicates solar cannot
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# be installed. We're loose about this now since this is post review
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if solar_api_client.panel_performance.empty:
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# We assume solar is a suitable option
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solar_api_client.panel_performance = solar_api_client.default_panel_performance(property_instance)
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# Store the data in the database
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solar_api_client.save_to_db(
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session=session,
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@ -923,12 +942,43 @@ class GoogleSolarApi:
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None
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)
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if material_1_6 is None or material_3_2 is None:
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material_4_35 = next(
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(m for m in self.solar_materials if m["type"] == "solar_pv" and
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abs(m["size"] - 4.35) < 0.1 and not m["includes_battery"]),
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None
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)
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if material_1_6 is None or material_3_2 is None or material_4_35 is None:
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raise ValueError("No suitable solar product found for the default configuration.")
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# We return a 1.6 and 3.2 kwp system
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panel_performance = pd.DataFrame(
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[
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{
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'n_panels': 10,
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'yearly_dc_energy': 4350 * assumptions.MEDIAN_WATTAGE_TO_DC,
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'total_cost': cost_instance.solar_pv(
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solar_product=material_4_35,
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scaffolding_options=[
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{"total_cost": 1000, "size": property_instance.number_of_floors},
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{"total_cost": 1000, "size": 3}
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],
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n_floors=property_instance.number_of_floors
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)["total"],
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'weighted_ratio': None,
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'panneled_roof_area': 9 * assumptions.RDSAP_AREA_PER_PANEL,
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'array_wattage': 4350,
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'initial_ac_kwh_per_year': 4350 * assumptions.MEDIAN_WATTAGE_TO_AC,
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'lifetime_ac_kwh': None,
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'lifetime_dc_kwh': None,
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'roi': None,
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'generation_value': None,
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'generation_deficit': None,
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'expected_payback_years': None,
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'surplus': None,
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'combined_score': None,
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'rank': None
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},
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{
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'n_panels': 8,
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'yearly_dc_energy': 3200 * assumptions.MEDIAN_WATTAGE_TO_DC,
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@ -139,7 +139,7 @@ def parse_eco_packages(config: dict[str, Any]) -> tuple[list[str], int, str] | t
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"plan_type": "solar_eco4"
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},
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"Solar Eligible, Needs Heating Upgrade": {
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"measures": ["solar_pv", "loft_insulation", "high_heat_retention_storage_heater"],
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"measures": ["solar_pv", "loft_insulation", "high_heat_retention_storage_heater", "mechanical_ventilation"],
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"target_sap": 86, # High B
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"plan_type": "solar_hhrsh_eco4"
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},
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@ -400,8 +400,13 @@ async def model_engine(body: PlanTriggerRequest):
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plan_input = plan_input.rename(
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columns={"domna_address_1": "address", "domna_postcode": "postcode", "epc_os_uprn": "uprn"}
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)
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# Where the EPC has been estimated, that is because a UPRN wasn't avaialble and so we remote UPRN
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plan_input["uprn"] = np.where(plan_input["estimated"].isin([1, True]), None, plan_input["uprn"])
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# Where the EPC has been estimated, that is because a UPRN wasn't avaialble and so we remove UPRN
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# This will be reflexted
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plan_input["uprn"] = np.where(
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plan_input["estimated"].isin([1, True]) & (
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(plan_input["uprn"] < 0) | pd.isnull(plan_input["uprn"])
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), None, plan_input["uprn"]
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)
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# We handle the landlord property type and built form
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plan_input["property_type"] = plan_input["landlord_property_type"].copy()
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if "landlord_built_form" in plan_input.columns:
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@ -512,7 +517,9 @@ async def model_engine(body: PlanTriggerRequest):
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epc_searcher.ordnance_survey_client.property_type = config.get("property_type", None)
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# For the moment, our OS API access is unavailable, so we skip and interpolate
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epc_searcher.find_property(skip_os=True)
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if epc_searcher.newest_epc.get("estimated") and body.file_format == "domna_asset_list":
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if epc_searcher.newest_epc.get("estimated") and body.file_format == "domna_asset_list" and (
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epc_searcher.newest_epc["uprn"] < 0
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):
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epc_searcher.newest_epc["uprn-source"] = epc_searcher.UPRN_SOURCE_SIMULATED
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# We check for an energy assessment we have performed on this property:
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@ -678,7 +685,7 @@ async def model_engine(body: PlanTriggerRequest):
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input_properties=input_properties,
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session=session,
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google_solar_api_key=get_settings().GOOGLE_SOLAR_API_KEY,
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solar_materials=[m for m in materials if m["type"] == "solar_pv"]
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solar_materials=[m for m in materials if m["type"] == "solar_pv"],
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)
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input_properties = GoogleSolarApi.unit_solar_analysis(
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@ -688,8 +695,12 @@ async def model_engine(body: PlanTriggerRequest):
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body=body,
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solar_materials=[m for m in materials if m["type"] == "solar_pv"],
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google_solar_api_key=get_settings().GOOGLE_SOLAR_API_KEY,
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inspections_map=inspections_map
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)
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# We also make a tweak - if the property has been flagged for solar but doesn't contain
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# any panel performance, we ensure that we have a 3kWp and 4kWp option for the property
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logger.info("Identifying property recommendations")
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recommendations = {}
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recommendations_scoring_data = []
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