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implemented unit level solar api
This commit is contained in:
Khalim Conn-Kowlessar
parent
754d46073e
commit
b85fde1b21
3 changed files with 71 additions and 106 deletions
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@ -159,7 +159,7 @@ class Property:
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self.floor_height = epc_record.prepared_epc.get("floor_height")
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self.insulation_wall_area = None
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self.floor_area = epc_record.prepared_epc.get("total_floor_area")
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self.pitched_roof_area = None
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self.roof_area = None
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self.insulation_floor_area = None
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self.number_lighting_outlets = epc_record.prepared_epc.get(
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"fixed_lighting_outlets_count"
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@ -604,18 +604,12 @@ class Property:
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def get_components(
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self,
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cleaned,
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photo_supply_lookup,
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floor_area_decile_thresholds,
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energy_consumption_client
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):
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"""
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Given the cleaning that has been performed, we'll use this to identify the property
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components, from roof to walls to windows, heating and hot water
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:param cleaned: This is the dictionary of components found in cleaner.cleaned
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:param photo_supply_lookup: This is the lookup table for the photo supply, used to estimate the percentage
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of the roof that is suitable for solar panels
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:param floor_area_decile_thresholds: This is the decile thresholds for the floor area, used in estimating the
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solar pv roof area
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:param energy_consumption_client: Contains the heating and hot water kwh models - used to predict current
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energy annual consumption in kWh
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:return:
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@ -680,20 +674,21 @@ class Property:
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self.set_floor_type()
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self.set_floor_level()
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self.set_windows_count()
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self.set_solar_panel_area(
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photo_supply_lookup=photo_supply_lookup,
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floor_area_decile_thresholds=floor_area_decile_thresholds,
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)
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self.set_energy_source()
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self.find_energy_sources()
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self.set_current_energy_bill(energy_consumption_client)
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def set_solar_panel_configuration(self, solar_panel_configuration):
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def set_solar_panel_configuration(
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self, solar_panel_configuration, roof_area
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):
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"""
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This funtion inserts the solar panel configuration into the property object
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"""
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self.solar_panel_configuration = solar_panel_configuration
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# We also set the roof area
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self.roof_area = roof_area
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def set_current_energy_bill(self, energy_consumption_client):
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"""
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Given what we know about the property now, estimates the current energy consumption using the UCL paper
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@ -1079,9 +1074,9 @@ class Property:
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if condition_data["main_dwelling_ground_floor_area"] is not None \
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else self.floor_area / self.number_of_floors
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self.pitched_roof_area = esimtate_pitched_roof_area(
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floor_area=self.insulation_floor_area, floor_height=self.floor_height
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)
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# self.pitched_roof_area = esimtate_pitched_roof_area(
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# floor_area=self.insulation_floor_area, floor_height=self.floor_height
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# )
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def set_floor_level(self):
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self.floor_level = (
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@ -1195,48 +1190,6 @@ class Property:
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if condition_data["windows_area"] is not None \
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else None
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def set_solar_panel_area(self, photo_supply_lookup, floor_area_decile_thresholds):
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"""
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Sets the approximate area of the solar panels
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:return:
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"""
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if (self.insulation_floor_area is None) and (self.pitched_roof_area is None):
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raise ValueError(
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"Need to set insulation floor area and pitched roof area before setting solar pv roof area"
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)
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photo_supply_matched = SolarPhotoSupply.filter_photo_supply_lookup(
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photo_supply_lookup=photo_supply_lookup,
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floor_area_decile_thresholds=floor_area_decile_thresholds,
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tenure=self.data["tenure"],
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built_form=self.data["built-form"],
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property_type=self.data["property-type"],
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construction_age_band=self.construction_age_band,
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is_flat=self.roof["is_flat"],
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is_pitched=self.roof["is_pitched"],
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is_roof_room=self.roof["is_roof_room"],
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floor_area=self.floor_area,
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)
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percentage_of_roof = photo_supply_matched["photo_supply_median"].mean()
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percentage_of_roof = percentage_of_roof / 100
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self.solar_pv_percentage = percentage_of_roof
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def get_solar_pv_roof_area(self, percentage_of_roof):
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"""
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Given a percentage of the roof, this method will return the estimated area of the solar panels
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:param percentage_of_roof:
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:return:
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"""
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return (
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self.insulation_floor_area * percentage_of_roof
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if self.roof["is_flat"]
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else self.pitched_roof_area * percentage_of_roof
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)
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def set_energy_source(self):
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"""
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This method sets the energy source of the property, based on the mains gas flag and energy tariff.
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@ -1335,6 +1288,23 @@ class Property:
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return suitable_property_type and not has_air_source_heat_pump
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def is_solar_pv_valid(self):
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# If the property is a flat but we are looking at building solar potential, we can include this
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if (self.building_id is not None) and (self.solar_panel_configuration is not None):
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return True
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is_valid_property_type = self.data["property-type"] in ["House", "Bungalow", "Maisonette"]
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is_valid_roof_type = (
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self.roof["is_flat"] or self.roof["is_pitched"] or self.roof["is_roof_room"]
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)
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# If there is no existing solar PV, the photo-supply field will be None or a missing value
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has_no_existing_solar_pv = self.data["photo-supply"] in [
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None, 0, self.DATA_ANOMALY_MATCHES
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]
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return is_valid_property_type and is_valid_roof_type and has_no_existing_solar_pv
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def estimate_electrical_consumption(self, assumed_ashp_efficiency, exclusions):
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"""
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Given a property, this method estimates the electrical consumption of the property, based on the energy
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@ -408,7 +408,6 @@ async def trigger_plan(body: PlanTriggerRequest):
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uprn_filenames = read_dataframe_from_s3_parquet(
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bucket_name=get_settings().DATA_BUCKET, file_key="spatial/filename_meta.parquet"
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)
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photo_supply_lookup, floor_area_decile_thresholds = SolarPhotoSupply.load(bucket=get_settings().DATA_BUCKET)
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solar_api_client = GoogleSolarApi(api_key=get_settings().GOOGLE_SOLAR_API_KEY)
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dataset_version = "2024-07-08"
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@ -425,10 +424,10 @@ async def trigger_plan(body: PlanTriggerRequest):
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logger.info("Getting spatial data")
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for p in input_properties:
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p.get_components(cleaned, photo_supply_lookup, floor_area_decile_thresholds, energy_consumption_client)
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p.get_components(cleaned=cleaned, energy_consumption_client=energy_consumption_client)
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p.get_spatial_data(uprn_filenames)
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# TODO: Handle the case of modelling some units as buildings and some as properties individually
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# TODO: Tidy this up
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building_ids = [
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{
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"building_id": p.building_id,
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@ -439,7 +438,7 @@ async def trigger_plan(body: PlanTriggerRequest):
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# property to achieve post retrofit of just the fabric
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"energy_consumption": energy_consumption_client.estimate_new_consumption(
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current_energy_efficiency=p.data["current-energy-efficiency"],
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target_efficiency="C",
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target_efficiency="69",
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current_consumption=p.estimate_electrical_consumption(
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assumed_ashp_efficiency=assumptions.AVERAGE_ASHP_EFFICIENCY, exclusions=body.exclusions
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)
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@ -448,6 +447,24 @@ async def trigger_plan(body: PlanTriggerRequest):
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"uprn": p.uprn
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} for p in input_properties if p.building_id is not None
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]
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individual_units = [
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{
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"longitude": p.spatial["longitude"],
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"latitude": p.spatial["latitude"],
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# Energy consumption is adjusted for the property's expected post retrofit state
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# We set the target rating to EPC C, which is the typical EPC rating we would expect the
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# property to achieve post retrofit of just the fabric
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"energy_consumption": energy_consumption_client.estimate_new_consumption(
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current_energy_efficiency=p.data["current-energy-efficiency"],
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target_efficiency="69",
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current_consumption=p.estimate_electrical_consumption(
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assumed_ashp_efficiency=assumptions.AVERAGE_ASHP_EFFICIENCY, exclusions=body.exclusions
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),
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),
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"property_id": p.id,
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"uprn": p.uprn
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} for p in input_properties if p.building_id is None
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]
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if building_ids:
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# Find the unique longitude and latitude pairs for each building id
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unique_coordinates = {}
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@ -511,32 +528,21 @@ async def trigger_plan(body: PlanTriggerRequest):
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)
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p.set_solar_panel_configuration(unit_solar_panel_configuration)
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else:
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if individual_units:
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# Model the solar potential at the property level
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for p in input_properties:
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# TODO: Complete me! - we probably won't do this for individual flats - IGNORE FLATS FROM THIS WITHOUT
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# BUILDING IDS
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electric_consumption = p.estimate_electrical_consumption(
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assumed_ashp_efficiency=assumptions.AVERAGE_ASHP_EFFICIENCY, exclusions=body.exclusions
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)
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# We now decrease this, based on the expected energy efficiency of the property post retrofit to a C,
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# which is the common level we would expect the property to reach when treating the fabric of the
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# home
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electric_consumption = energy_consumption_client.estimate_new_consumption(
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current_energy_efficiency=p.data["current-energy-efficiency"],
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target_efficiency="69",
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current_consumption=electric_consumption
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)
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for unit in individual_units:
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property_instance = [p for p in input_properties if p.id == unit["property_id"]][0]
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# At this level, we check if the property is suitable for solar and if now, skip
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if not property_instance.is_solar_pv_valid():
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continue
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solar_api_client.get(
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longitude=p.spatial["longitude"],
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latitude=p.spatial["latitude"],
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energy_consumption=electric_consumption,
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longitude=unit["longitude"],
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latitude=unit["latitude"],
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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=p.uprn
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uprn=unit["uprn"]
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)
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# Store the data in the database
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@ -544,16 +550,22 @@ async def trigger_plan(body: PlanTriggerRequest):
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solar_api_client.save_to_db(
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session=session,
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uprns_to_location=[
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{"uprn": p.uprn, "longitude": p.spatial["longitude"], "latitude": p.spatial["latitude"]}
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{
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"uprn": property_instance.uprn,
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"longitude": property_instance.spatial["longitude"],
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"latitude": property_instance.spatial["latitude"]
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}
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],
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scenario_type="unit"
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)
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# TODO: Insert the pitched roof area into the property class as we store the solar performance
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# in the property class
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print("Implement me")
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# TODO: We can set the pitched roof area based on the results of the solar api!
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property_instance.set_solar_panel_configuration(
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solar_panel_configuration={
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"insights_data": solar_api_client.insights_data,
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"panel_performance": solar_api_client.panel_performance
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},
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roof_area=solar_api_client.roof_area
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)
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logger.info("Getting components and epc recommendations")
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recommendations = {}
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@ -78,23 +78,6 @@ class SolarPvRecommendations:
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}
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]
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def is_solar_pv_valid(self):
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# If the property is a flat but we are looking at building solar potential, we can include this
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if (self.property.building_id is not None) and (self.property.solar_panel_configuration is not None):
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return True
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is_valid_property_type = self.property.data["property-type"] in ["House", "Bungalow", "Maisonette"]
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is_valid_roof_type = (
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self.property.roof["is_flat"] or self.property.roof["is_pitched"] or self.property.roof["is_roof_room"]
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)
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# If there is no existing solar PV, the photo-supply field will be None or a missing value
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has_no_existing_solar_pv = self.property.data["photo-supply"] in [
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None, 0, self.property.DATA_ANOMALY_MATCHES
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]
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return is_valid_property_type and is_valid_roof_type and has_no_existing_solar_pv
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def recommend_building_analysis(self, phase):
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"""
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This recommendation approach handles the case of producing solar PV recommendations at the building level,
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@ -159,7 +142,7 @@ class SolarPvRecommendations:
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:return:
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"""
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if not self.is_solar_pv_valid():
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if not self.property.is_solar_pv_valid():
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return
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# If we have a buiilding level analysis, we implement separate logic
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