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added tests for final property
This commit is contained in:
Khalim Conn-Kowlessar
parent
5243e64e41
commit
53e68f8d76
3 changed files with 218 additions and 110 deletions
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@ -57,10 +57,18 @@ def app():
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['none', "below average"]
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)
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epc_data["needs_solid_wall"] = (epc_data["is_solid_brick"] | epc_data["is_system_built"]) & epc_data[
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"insulation_thickness_wall"].isin(['none', "below average"])
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epc_data["could_take_solar"] = (epc_data["is_flat"] | epc_data["is_pitched"])
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loft_insulation_per_m2 = 16.07
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flat_roof_insulation_per_m2 = 195
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cwi_per_m2 = 14.21
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ewi_per_m2 = 200
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gbis_abs = 30
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eco4_abs = 24
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solar_pv_cost = 4009
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# We assume the work will take the home from a high D to a low D
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def get_abs(floor_area):
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@ -75,6 +83,19 @@ def app():
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return 350.1
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# We assume the work will take the home from a high E to a high C
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def get_eco4_abs(floor_area):
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if floor_area <= 72:
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return 596.6
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if floor_area <= 97:
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return 650.2
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if floor_area <= 199:
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return 755.8
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return 1347.1
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estimated_costs = []
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for _, home in epc_data.iterrows():
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to_append = {
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@ -89,30 +110,59 @@ def app():
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n_floors = estimate_number_of_floors(home["PROPERTY_TYPE"])
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floor_height = float(home["FLOOR_HEIGHT"]) if not pd.isnull(home["FLOOR_HEIGHT"]) else 2.5
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# Check if it needs the walls done
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if home["needs_cavity_done"]:
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# We estimate the amount of insulation required
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est_perimeter = estimate_perimeter(
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floor_area=float(home["TOTAL_FLOOR_AREA"]) / n_floors,
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num_rooms=float(home["NUMBER_HABITABLE_ROOMS"]) / n_floors
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)
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# We estimate the amount of insulation required
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est_perimeter = estimate_perimeter(
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floor_area=float(home["TOTAL_FLOOR_AREA"]) / n_floors,
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num_rooms=float(home["NUMBER_HABITABLE_ROOMS"]) / n_floors
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)
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insulation_needed = estimate_external_wall_area(
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num_floors=n_floors,
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floor_height=floor_height,
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perimeter=est_perimeter,
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built_form=home["BUILT_FORM"],
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)
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cost_of_insulation = insulation_needed * cwi_per_m2
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insulation_needed = estimate_external_wall_area(
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num_floors=n_floors,
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floor_height=floor_height,
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perimeter=est_perimeter,
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built_form=home["BUILT_FORM"],
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)
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if available_funding > cost_of_insulation:
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available_funding = cost_of_insulation
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# At the very least we'll need solid wall + solar
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if home["needs_solid_wall"] and home["could_take_solar"]:
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measure = "EWI + Solar"
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total_cost = insulation_needed * ewi_per_m2 + solar_pv_cost
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eco4_project_abs = get_eco4_abs(home["TOTAL_FLOOR_AREA"])
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eco4_available_funding = eco4_project_abs * eco4_abs
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cost_of_work_after_funding = total_cost - eco4_available_funding
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cost_of_work_after_funding = 0 if cost_of_work_after_funding < 0 else cost_of_work_after_funding
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to_append = {
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**to_append,
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"scheme": "eco4",
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"available_funding": eco4_available_funding,
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"measure": measure,
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"project_abs": eco4_project_abs,
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"cost_of_work": total_cost,
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"cost_of_work_after_funding": cost_of_work_after_funding,
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}
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estimated_costs.append(to_append)
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continue
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# Check if it needs the walls done
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if home["needs_cavity_done"]:
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cost_of_insulation = insulation_needed * cwi_per_m2
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cost_of_work_after_funding = cost_of_insulation - available_funding
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cost_of_work_after_funding = 0 if cost_of_work_after_funding < 0 else cost_of_work_after_funding
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to_append = {
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**to_append,
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"scheme": "gbis",
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"available_funding": available_funding,
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"measure": "Cavity Wall Insulation",
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"project_abs": project_abs
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"project_abs": project_abs,
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"cost_of_work": cost_of_insulation,
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"cost_of_work_after_funding": cost_of_work_after_funding
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}
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estimated_costs.append(to_append)
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@ -130,14 +180,17 @@ def app():
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roof_area = float(home["TOTAL_FLOOR_AREA"]) / n_floors
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cost_of_insulation = roof_area * flat_roof_insulation_per_m2
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if available_funding > cost_of_insulation:
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available_funding = cost_of_insulation
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cost_of_work_after_funding = cost_of_insulation - available_funding
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cost_of_work_after_funding = 0 if cost_of_work_after_funding < 0 else cost_of_work_after_funding
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to_append = {
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**to_append,
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"scheme": "gbis",
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"available_funding": available_funding,
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"measure": measure,
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"project_abs": project_abs
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"project_abs": project_abs,
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"cost_of_work": cost_of_insulation,
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"cost_of_work_after_funding": cost_of_work_after_funding
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}
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estimated_costs.append(to_append)
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@ -145,13 +198,10 @@ def app():
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estimated_costs = pd.DataFrame(estimated_costs)
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estimated_costs.groupby("measure")["available_funding"].mean()
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estimated_costs["measure"].value_counts()
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estimated_costs.to_csv("/Users/khalimconn-kowlessar/Documents/hestia/Customers/sfr/estimated_costs_gbis.csv")
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epc_data[["UPRN", "ADDRESS", "POSTCODE"]].to_csv(
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"/Users/khalimconn-kowlessar/Documents/hestia/sfr/council_tax_bands_sample.csv")
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# epc_data[["UPRN", "ADDRESS", "POSTCODE"]].to_csv(
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# "/Users/khalimconn-kowlessar/Documents/hestia/sfr/council_tax_bands_sample.csv")
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n_properties_for_ashp = epc_data[
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(epc_data["PROPERTY_TYPE"] == "House") &
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@ -91,7 +91,7 @@ class HeatingRecommender:
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# If the property already has room heaters then we recommend HHR as an option since the home already has
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# a variation of room heaters
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hhr_suitable = no_mains or self.has_electric_heating_description or self.has_room_heaters
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return (
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@ -130,6 +130,13 @@ class HeatingRecommender:
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not self.property.main_heating["has_mains_gas"] and
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self.property.data["mains-gas-flag"]
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)
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# Additionally, if the property has a gas connection, is using gas heating but doesn't have a boiler,
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# we recommend a boiler
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non_boiler_gas_heating = (
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self.property.data["mains-gas-flag"] and
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self.property.main_heating["has_mains_gas"] and
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not self.property.main_heating["has_boiler"]
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)
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is_valid = (
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(
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@ -138,7 +145,8 @@ class HeatingRecommender:
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electic_heating_has_mains or
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has_room_heaters or
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portable_heaters_has_mains or
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non_gas_boiler
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non_gas_boiler or
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non_boiler_gas_heating
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) and
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(not ashp_only_heating_recommendation) and
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("boiler_upgrade" in measures) and
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@ -842,12 +850,13 @@ class HeatingRecommender:
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has_inefficient_space_heating = self.property.data["mainheat-energy-eff"] in ["Very Poor", "Poor", "Average"]
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has_inefficient_mains_water = (
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self.property.hotwater["clean_description"] in ["From main system"] and
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# We check if there's a mains connection and the hot water is inefficient, as this will improve with a boiler
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has_inefficient_water = (
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self.property.data["mains-gas-flag"] and
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self.property.data["hot-water-energy-eff"] in ["Very Poor", "Poor", "Average"]
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)
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if has_inefficient_space_heating or has_inefficient_mains_water:
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if has_inefficient_space_heating or has_inefficient_water:
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boiler_size = self.estimate_boiler_size(
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property_type=self.property.data["property-type"],
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built_form=self.property.data["built-form"],
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@ -1364,86 +1364,135 @@ testing_examples = [
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"heating_controls_recommendation_descriptions": [],
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"notes": "The property has warm air electricaire heating, so we recommend ASHP and HHR. It also has a mains"
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"connection so we recommend a gas condensing boiler"
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},
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{
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"epc": {
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'lmk-key': '272170070262009042917361440218801', 'address1': '52, Chiswick Walk', 'address2': None,
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'address3': None, 'postcode': 'B37 6TA', 'building-reference-number': 479790668,
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'current-energy-rating': 'F', 'potential-energy-rating': 'E', 'current-energy-efficiency': 31,
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'potential-energy-efficiency': 50, 'property-type': 'Flat', 'built-form': 'End-Terrace',
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'inspection-date': '2009-04-29', 'local-authority': 'E08000029', 'constituency': 'E14000812',
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'county': None,
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'lodgement-date': '2009-04-29', 'transaction-type': 'marketed sale', 'environment-impact-current': 37,
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'environment-impact-potential': 42, 'energy-consumption-current': 548, 'energy-consumption-potential': 459,
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'co2-emissions-current': 5.8, 'co2-emiss-curr-per-floor-area': 89, 'co2-emissions-potential': 5.0,
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'lighting-cost-current': 60, 'lighting-cost-potential': 30, 'heating-cost-current': 751,
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'heating-cost-potential': 601, 'hot-water-cost-current': 239, 'hot-water-cost-potential': 129,
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'total-floor-area': 65.04, 'energy-tariff': 'Single', 'mains-gas-flag': 'Y', 'floor-level': '1st',
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'flat-top-storey': 'Y', 'flat-storey-count': 1.0, 'main-heating-controls': 2504.0,
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'multi-glaze-proportion': 100.0, 'glazed-type': 'double glazing installed during or after 2002',
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'glazed-area': 'Normal', 'extension-count': 0, 'number-habitable-rooms': 3, 'number-heated-rooms': 2,
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'low-energy-lighting': 0, 'number-open-fireplaces': 0,
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'hotwater-description': 'Electric immersion, standard tariff', 'hot-water-energy-eff': 'Very Poor',
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'hot-water-env-eff': 'Poor', 'floor-description': 'To external air, no insulation (assumed)',
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'floor-energy-eff': None, 'windows-description': 'Fully double glazed', 'windows-energy-eff': 'Good',
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'windows-env-eff': 'Good', 'walls-description': 'System built, as built, no insulation (assumed)',
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'walls-energy-eff': 'Very Poor', 'walls-env-eff': 'Very Poor',
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'secondheat-description': 'Portable electric heaters',
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'roof-description': 'Pitched, limited insulation (assumed)', 'roof-energy-eff': 'Very Poor',
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'roof-env-eff': 'Very Poor', 'mainheat-description': 'Warm air, mains gas', 'mainheat-energy-eff': 'Good',
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'mainheat-env-eff': 'Good', 'mainheatcont-description': 'Programmer and room thermostat',
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'mainheatc-energy-eff': 'Average', 'mainheatc-env-eff': 'Average',
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'lighting-description': 'No low energy lighting', 'lighting-energy-eff': 'Very Poor',
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'lighting-env-eff': 'Very Poor',
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'main-fuel': 'mains gas - this is for backwards compatibility only and should not be used',
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'wind-turbine-count': 0, 'heat-loss-corridor': 'unheated corridor', 'unheated-corridor-length': 5.63,
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'floor-height': 2.32, 'photo-supply': 0.0, 'solar-water-heating-flag': 'N',
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'mechanical-ventilation': 'natural', 'address': '52, Chiswick Walk', 'local-authority-label': 'Solihull',
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'constituency-label': 'Meriden', 'posttown': 'BIRMINGHAM',
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'construction-age-band': 'England and Wales: 1967-1975', 'lodgement-datetime': '2009-04-29 17:36:14',
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'tenure': 'owner-occupied', 'fixed-lighting-outlets-count': None, 'low-energy-fixed-light-count': None,
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'uprn': 100070955137, 'uprn-source': 'Address Matched', 'sheating-energy-eff': None,
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'sheating-env-eff': None
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},
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"heating_recommendation_descriptions": [
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'Upgrade to a new condensing boiler Upgrade heating controls to Room thermostat, programmer and TRVs',
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'Upgrade to a new condensing boiler Upgrade heating controls to Smart Thermostats, room sensors and smart '
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'radiator valves (time & temperature zone control)'
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],
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"heating_controls_recommendation_descriptions": [],
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"notes": "This property has warm air mains gas heating, so we recommend a gas condensing boiler"
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}
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]
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import random
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from pathlib import Path
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import inspect
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import pandas as pd
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# this can be used to get example data to build the test cases
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src_file_path = inspect.getfile(lambda: None)
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EPC_DIRECTORY = Path(src_file_path).parent / "local_data" / "all-domestic-certificates"
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epc_directories = [entry for entry in EPC_DIRECTORY.iterdir() if entry.is_dir()]
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directory = random.sample(epc_directories, 1)[0]
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data = pd.read_csv(directory / "certificates.csv", low_memory=False)
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# Rename the columns to the same format as the api returns
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data.columns = [c.replace("_", "-").lower() for c in data.columns]
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data["floor-height"] = data["floor-height"].fillna(2.45)
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used_examples = pd.DataFrame(
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[
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{
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"mainheat-description": x["epc"]["mainheat-description"],
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"mainheat-energy-eff": x["epc"]["mainheat-energy-eff"],
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"property-type": x["epc"]["property-type"],
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"built-form": x["epc"]["built-form"],
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"used": True
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} for x in testing_examples
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]
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)
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data = data.merge(
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used_examples, how="left", on=["mainheat-description", "mainheat-energy-eff", "built-form", "property-type"]
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)
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data = data[pd.isnull(data["used"])].drop(columns=["used"])
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eg = data.sample(1).to_dict("records")[0]
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print(eg["mainheat-description"])
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print(eg["mainheat-energy-eff"])
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print(eg["property-type"])
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print(eg["built-form"])
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print(eg["mainheatcont-description"])
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### We also use the Midlands EPC F/G portfolio to get examples to create tests
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completed_descriptions = [
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"Portable electric heaters assumed for most rooms",
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"Boiler and radiators, oil",
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"Boiler and radiators, mains gas",
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"Room heaters, mains gas",
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"No system present: electric heaters assumed",
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"Room heaters, electric",
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"Electric storage heaters",
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"Boiler and radiators, LPG",
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"Boiler and radiators, electric",
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"Boiler and radiators, dual fuel (mineral and wood)",
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"Boiler and radiators, coal",
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"Boiler and radiators, smokeless fuel",
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"Boiler and radiators, wood pellets",
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"Room heaters, dual fuel (mineral and wood)",
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"Air source heat pump, radiators, electric",
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"Portable electric heaters assumed for most rooms, Room heaters, electric",
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"Boiler and radiators, mains gas, Electric storage heaters",
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"Room heaters, anthracite",
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"Room heaters, mains gas, Room heaters, dual fuel (mineral and wood)",
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"Electric underfloor heating",
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]
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portfolio = pd.read_excel(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/sfr/20240820 portfolio_epc_data.xlsx"
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)
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portfolio.columns = [c.replace("_", "-").lower() for c in portfolio.columns]
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portfolio = portfolio[~portfolio["mainheat-description"].isin(completed_descriptions)]
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portfolio['sheating-energy-eff'] = None
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portfolio['sheating-env-eff'] = None
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portfolio["lodgement-datetime"] = portfolio["lodgement-datetime"].astype(str)
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print(portfolio["mainheat-description"].value_counts())
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eg = portfolio[
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(portfolio["mainheat-description"] == "Warm air, Electricaire")
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].sample(1)
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eg = eg.squeeze().to_dict()
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print(eg)
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# import random
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# from pathlib import Path
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# import inspect
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# import pandas as pd
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#
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# # this can be used to get example data to build the test cases
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# src_file_path = inspect.getfile(lambda: None)
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# EPC_DIRECTORY = Path(src_file_path).parent / "local_data" / "all-domestic-certificates"
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# epc_directories = [entry for entry in EPC_DIRECTORY.iterdir() if entry.is_dir()]
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# directory = random.sample(epc_directories, 1)[0]
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# data = pd.read_csv(directory / "certificates.csv", low_memory=False)
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# # Rename the columns to the same format as the api returns
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# data.columns = [c.replace("_", "-").lower() for c in data.columns]
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# data["floor-height"] = data["floor-height"].fillna(2.45)
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#
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# used_examples = pd.DataFrame(
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# [
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# {
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# "mainheat-description": x["epc"]["mainheat-description"],
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# "mainheat-energy-eff": x["epc"]["mainheat-energy-eff"],
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# "property-type": x["epc"]["property-type"],
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# "built-form": x["epc"]["built-form"],
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# "used": True
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# } for x in testing_examples
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# ]
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# )
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#
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# data = data.merge(
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# used_examples, how="left", on=["mainheat-description", "mainheat-energy-eff", "built-form", "property-type"]
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# )
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# data = data[pd.isnull(data["used"])].drop(columns=["used"])
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#
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# eg = data.sample(1).to_dict("records")[0]
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# print(eg["mainheat-description"])
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# print(eg["mainheat-energy-eff"])
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# print(eg["property-type"])
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# print(eg["built-form"])
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# print(eg["mainheatcont-description"])
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#
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# ### We also use the Midlands EPC F/G portfolio to get examples to create tests
|
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#
|
||||
# completed_descriptions = [
|
||||
# "Portable electric heaters assumed for most rooms",
|
||||
# "Boiler and radiators, oil",
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||||
# "Boiler and radiators, mains gas",
|
||||
# "Room heaters, mains gas",
|
||||
# "No system present: electric heaters assumed",
|
||||
# "Room heaters, electric",
|
||||
# "Electric storage heaters",
|
||||
# "Boiler and radiators, LPG",
|
||||
# "Boiler and radiators, electric",
|
||||
# "Boiler and radiators, dual fuel (mineral and wood)",
|
||||
# "Boiler and radiators, coal",
|
||||
# "Boiler and radiators, smokeless fuel",
|
||||
# "Boiler and radiators, wood pellets",
|
||||
# "Room heaters, dual fuel (mineral and wood)",
|
||||
# "Air source heat pump, radiators, electric",
|
||||
# "Portable electric heaters assumed for most rooms, Room heaters, electric",
|
||||
# "Boiler and radiators, mains gas, Electric storage heaters",
|
||||
# "Room heaters, anthracite",
|
||||
# "Room heaters, mains gas, Room heaters, dual fuel (mineral and wood)",
|
||||
# "Electric underfloor heating",
|
||||
# "Warm air, Electricaire"
|
||||
# ]
|
||||
#
|
||||
# portfolio = pd.read_excel(
|
||||
# "/Users/khalimconn-kowlessar/Documents/hestia/Customers/sfr/20240820 portfolio_epc_data.xlsx"
|
||||
# )
|
||||
# portfolio.columns = [c.replace("_", "-").lower() for c in portfolio.columns]
|
||||
# portfolio = portfolio[~portfolio["mainheat-description"].isin(completed_descriptions)]
|
||||
# portfolio['sheating-energy-eff'] = None
|
||||
# portfolio['sheating-env-eff'] = None
|
||||
# portfolio["lodgement-datetime"] = portfolio["lodgement-datetime"].astype(str)
|
||||
#
|
||||
# print(portfolio["mainheat-description"].value_counts())
|
||||
#
|
||||
# eg = portfolio[
|
||||
# (portfolio["mainheat-description"] == "Warm air, mains gas")
|
||||
# ].sample(1)
|
||||
# eg = eg.squeeze().to_dict()
|
||||
# print(eg)
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue