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minor tweaks and bug fixes for properties that failed diagnostic tests
This commit is contained in:
Khalim Conn-Kowlessar
parent
45170d4724
commit
608ff71d35
3 changed files with 102 additions and 17 deletions
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@ -23,9 +23,10 @@ scenario_sap_targets = {
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859: 69,
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}
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problems = []
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for scenario_id, scenario_name in scenario_names.items():
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# Read in the recommended measures
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print("Reading")
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df = pd.read_excel(
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f"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/"
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f"{scenario_name}.xlsx"
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@ -34,30 +35,98 @@ for scenario_id, scenario_name in scenario_names.items():
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# find properties that are below the scenario sap target, but have no recommended measures
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df["below_scenario_target"] = df["current_sap_points"] < scenario_sap_targets[scenario_id]
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df["no_recommended_measures"] = df["sap_points"] == 0
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df["zero_cost"] = df["total_retrofit_cost"] == 0
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df["sap_points_above_zero"] = df["sap_points"] > 0
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# Also look for zero cost and SAP points > 0
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problematic_properties = df[
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df["below_scenario_target"] & df["no_recommended_measures"]
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]
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(df["below_scenario_target"] & df["no_recommended_measures"])
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].copy()
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if scenario_sap_targets[scenario_id] == 81:
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problematic_properties = problematic_properties[problematic_properties["property_type"] != "Flat"]
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zero_cost_above_zero_sap = df[
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(df["sap_points_above_zero"] & df["zero_cost"])
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].copy()
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# show all columns
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# Source - https://stackoverflow.com/a
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# Posted by YOLO, modified by community. See post 'Timeline' for change history
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# Retrieved 2026-01-06, License - CC BY-SA 4.0
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pd.set_option('display.max_rows', 500)
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pd.set_option('display.max_columns', 500)
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pd.set_option('display.width', 1000)
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problematic_properties.head(len(problematic_properties))
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# pd.set_option('display.max_rows', 500)
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# pd.set_option('display.max_columns', 500)
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# pd.set_option('display.width', 1000)
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# problematic_properties.head(len(problematic_properties))
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#
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print(f"We have {len(problematic_properties)} problematic properties for scenario {scenario_name} ({scenario_id})")
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print(f"We have {len(zero_cost_above_zero_sap)} zero cost properties for scenario {scenario_name} ({scenario_id})")
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problems.append(problematic_properties)
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problems.append(zero_cost_above_zero_sap)
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# plan_input = [
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# {
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# "uprn": 100022725126,
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# "address": "FLAT 5 Daveys Court",
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# "postcode": "WC2N 4BW"
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# }
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# ]
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# plan_input = [
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# {
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# "uprn": 100120966352,
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# "address": "FLAT 11 Kingsgate",
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# "postcode": "OX18 2BP"
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# }
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# ]
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plan_input = [
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{
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"uprn": 100022725126,
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"address": "FLAT 5 Daveys Court",
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"postcode": "WC2N 4BW"
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"uprn": 200003371857,
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"postcode": "SE1 5SJ",
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"address": "39 BUTTERMERE CLOSE",
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}
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]
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all_problems = pd.concat(problems)
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all_problems = all_problems.drop_duplicates(subset=["uprn"])
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sal = pd.read_excel(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/20251213 Model "
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"data.xlsx",
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sheet_name="Standardised Asset List"
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)
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sal2 = pd.read_excel(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/20260105 - additional "
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"UPRNS.xlsx",
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sheet_name="Standardised Asset List"
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)
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sal = pd.concat([sal, sal2])
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retry = sal[sal["epc_os_uprn"].isin(all_problems["uprn"])]
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# Store
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retry.to_excel(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/"
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"d_problematic_properties_to_review_20260106.xlsx",
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sheet_name="Standardised Asset List",
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index=False
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)
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# Delete associated plans
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# 1) Get the property IDs for these UPRNS, for this portfolio
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portfolio_id = 419
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uprns = retry
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# TODO: Delete all plans for these properties and re-build
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# Plan notes:
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# UPRN: 5870109770, property ID: 281244 - need to delete and re-build all scenarios
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# UPRN: 100022725126, property ID: 283781 - need to delete and re-build all scenarios
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# Bugs:
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12156800
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@ -170,9 +170,14 @@ class HeatingRecommender:
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# If the property has community heating heaters in place, we don't recommend HHRSH
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has_community_heating = self.property.main_fuel["is_community"]
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hhr_suitable = hhr_suitable and (
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"underfloor heating" not in self.property.main_heating["clean_description"]
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) and not has_community_heating
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# If the property currently has electric underfloor heating, we allow this if there is elecric immersion
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# hot water heating
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underfloor_not_an_issue = True
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if self.property.main_heating["has_electric_underfloor_heating"]:
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if self.property.hotwater["heater_type"] != "electric immersion":
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underfloor_not_an_issue = False
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hhr_suitable = hhr_suitable and not has_community_heating and underfloor_not_an_issue
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# If the property has a ground source heat pump, or air source heat pump, we don't recommend HHRSH
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@ -86,9 +86,17 @@ class WindowsRecommendations:
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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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int(self.property.data["multi-glaze-proportion"]) / 100
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)
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if (self.property.windows["clean_description"] == "Some double glazing") and (
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self.property.data["windows-energy-eff"] == "Very Poor") and (
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self.property.data["multi-glaze-proportion"] == 100
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):
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# In this case, we assume all of the dinwos need replacing
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n_windows_scalar = 1
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else:
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n_windows_scalar = 1 - (
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int(self.property.data["multi-glaze-proportion"]) / 100
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)
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else:
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n_windows_scalar = self.COVERAGE_MAP.get(
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self.property.windows["glazing_coverage"], 1
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@ -97,6 +105,9 @@ class WindowsRecommendations:
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number_of_windows *= n_windows_scalar
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number_of_windows = np.ceil(number_of_windows)
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# Handle edge case - prevent number of windows 0
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number_of_windows = max(1, number_of_windows)
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# We then price the job based on the number of windows that there are
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cost_result = self.costs.window_glazing(
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number_of_windows=number_of_windows,
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