mirror of
https://github.com/Hestia-Homes/Model.git
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fixing ventilation negative kwh
This commit is contained in:
Khalim Conn-Kowlessar
parent
746c42594c
commit
1d48ede60e
8 changed files with 141 additions and 68 deletions
2
.idea/Model.iml
generated
2
.idea/Model.iml
generated
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@ -7,7 +7,7 @@
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<sourceFolder url="file://$MODULE_DIR$/open_uprn" isTestSource="false" />
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<sourceFolder url="file://$MODULE_DIR$/recommendations" isTestSource="false" />
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</content>
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<orderEntry type="jdk" jdkName="AssetList" jdkType="Python SDK" />
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<orderEntry type="jdk" jdkName="Fastapi-backend" jdkType="Python SDK" />
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<orderEntry type="sourceFolder" forTests="false" />
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</component>
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<component name="PyNamespacePackagesService">
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2
.idea/misc.xml
generated
2
.idea/misc.xml
generated
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@ -3,7 +3,7 @@
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<component name="Black">
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<option name="sdkName" value="Python 3.10 (backend)" />
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</component>
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<component name="ProjectRootManager" version="2" project-jdk-name="AssetList" project-jdk-type="Python SDK" />
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<component name="ProjectRootManager" version="2" project-jdk-name="Fastapi-backend" project-jdk-type="Python SDK" />
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<component name="PyCharmProfessionalAdvertiser">
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<option name="shown" value="true" />
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</component>
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@ -1249,20 +1249,19 @@ class AssetList:
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(self.standardised_asset_list["non-intrusives: Insulated"].isin(["RETRO DRILLED", "FILLED AT BUILD"])) &
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(~self.standardised_asset_list['non-intrusives: Material'].isin(
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["GREY LOOSE BEAD", "COMPACTED BEAD", "FIBRE BATT NO CAVITY", "EMPTY NARROW BELOW 30mm"]
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)
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)
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))
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)
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self.standardised_asset_list["non_intrusive_indicates_cavity_extraction"] = (
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extraction_wall_filter & (
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self.standardised_asset_list[self.ATTRIBUTE_SAP_THRESHOLD_AND_BELOW]
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)
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)
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))
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# Also include work without the SAP filter as optimistic
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self.standardised_asset_list["non_intrusive_indicates_cavity_extraction_no_sap_filter"] = (
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extraction_wall_filter
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)
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extraction_wall_filter & (
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~self.standardised_asset_list[self.ATTRIBUTE_SAP_THRESHOLD_AND_BELOW]
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))
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elif self.old_format_non_intrusives_present:
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print("Review these categories with Kieran")
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@ -418,7 +418,7 @@ def app():
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epc_df = pd.concat(epc_data)
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epc_df["estimated"] = epc_df["estimated"].fillna(False)
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z = epc_df[epc_df["domna_property_id"] == eg["domna_property_id"].values[0]]
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epc_df["number-habitable-rooms"].mean() + 1
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# We expand out the recommendations
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recommendations_df = epc_df[[asset_list.DOMNA_PROPERTY_ID, "recommendations"]]
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@ -545,26 +545,19 @@ def app():
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right_on=asset_list.STANDARD_LANDLORD_PROPERTY_ID
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)
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cavity_fills["cavity_reason"] = cavity_fills["cavity_reason"].fillna("Not identified")
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cavity_fills["cavity_reason"].value_counts()
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print(cavity_fills["cavity_reason"].value_counts())
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# Didn't identify 3 properties because they're bedsits
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# 4 properties were identified, not based on the non-intrusives but instead because
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# Westward said they were built in 2003/2007. Have adjusted this to use the age from the
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# epc as well, as EPC says 1975 and they look like 1975 properties
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# 58 properties flagged as already having solar:
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#
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z = cavity_fills[
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cavity_fills["cavity_reason"] == "Non-Intrusive Data Showed Empty Cavity - property already has solar"
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]
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df = asset_list.standardised_asset_list[
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asset_list.standardised_asset_list[asset_list.STANDARD_LANDLORD_PROPERTY_ID].isin(
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z[asset_list.landlord_property_id].values)
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]
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eg = df[df[asset_list.STANDARD_LANDLORD_PROPERTY_ID] == "TOTNEWINA0102300"]
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z[["Address", "WFT EDIT Postcode", asset_list.landlord_property_id]]
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z[[asset_list.STANDARD_FULL_ADDRESS, asset_list.STANDARD_POSTCODE, asset_list.ATTRIBUTE_HAS_SOLAR]]
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# 37 properties flagged as already having solar - these are all because the landlord said they have solar
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# e.g.
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# https://earth.google.com/web/search/11+Winsland+Avenue+TOTNES+TQ9+5FT/@50.43354465,-3.71318276,46.57468503a,
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# 59.14004365d,35y,0h,0t,
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# 0r/data=CpABGmISXAolMHg0ODZkMWQxOGE4NWRiZjdkOjB4YjBhM2E5M2Q3YWVlMWEwYhlZYgp7fzdJQCHFfC9027QNwCohMTEgV2luc2xhbmQgQXZlbnVlIFRPVE5FUyBUUTkgNUZUGAIgASImCiQJbxsQEoo3SUARXQcp_HE3SUAZBmiZGJ6yDcAhCA0fqq63DcBCAggBOgMKATBCAggASg0I____________ARAA
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# https://earth.google.com/web/search/15+St+Anne%27s+Ct,+Newton+Abbot+TQ12+1TL/@50.53068337,-3.61611128,
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# 11.74908956a,135.73212429d,35y,0h,0t,
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# 0r/data=CpUBGmcSYQolMHg0ODZkMDVkMjFhODhjZjgxOjB4MjBmMzE2Zjc3MGI2NGMwYxlCxHLw8UNJQCFZqyzALe4MwComMTUgU3QgQW5uZSdzIEN0LCBOZXd0b24gQWJib3QgVFExMiAxVEwYAiABIiYKJAm-r6U2iDdJQBHS5ICRdDdJQBmYGVpmiLINwCG8wcrtqbYNwEICCAE6AwoBMEICCABKDQj___________8BEAA
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# Check 2)
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cavity_fills_with_solar = pd.read_excel(
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@ -580,37 +573,51 @@ def app():
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right_on=asset_list.STANDARD_LANDLORD_PROPERTY_ID
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)
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cavity_fills_with_solar["cavity_reason"] = cavity_fills_with_solar["cavity_reason"].fillna("Not identified")
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print(cavity_fills_with_solar["cavity_reason"].value_counts())
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# 203 properties total
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# 140 properties were flagged up based on non-intrusives (Non-Intrusive Data Showed Empty Cavity)
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# 63 property already has solar
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check = cavity_fills_with_solar[
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cavity_fills_with_solar["cavity_reason"] == "Non-Intrusive Data Showed Empty Cavity"
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]
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z = asset_list.standardised_asset_list[
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asset_list.standardised_asset_list[asset_list.STANDARD_LANDLORD_PROPERTY_ID].isin(
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check[asset_list.landlord_property_id].values)
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# Check 3) RDF
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rdf = pd.read_excel(
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os.path.join(data_folder, "WESTWARD - Route March Prep.xlsx"),
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sheet_name="RDF CIGA checks"
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)
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rdf = rdf.merge(
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asset_list.standardised_asset_list[
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[asset_list.STANDARD_LANDLORD_PROPERTY_ID, "cavity_reason", "solar_reason"]
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],
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how="left",
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left_on=asset_list.landlord_property_id,
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right_on=asset_list.STANDARD_LANDLORD_PROPERTY_ID
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)
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rdf["cavity_reason"] = rdf["cavity_reason"].fillna("Not identified")
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print(rdf["cavity_reason"].value_counts())
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# 264 properties are not identified, 261 of which are due to the fact they contain materials
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# The other 3 were determined to be eligible for solar instead
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# Many of these units that were identified for rdf works could be solar jobs
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rdf_with_solar = pd.read_excel(
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os.path.join(data_folder, "WESTWARD - Route March Prep.xlsx"),
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sheet_name="Solar PV - RDF CIGA Checks"
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)
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rdf_with_solar = rdf_with_solar.merge(
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asset_list.standardised_asset_list[
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[asset_list.STANDARD_LANDLORD_PROPERTY_ID, "cavity_reason", "solar_reason"]
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],
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how="left",
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left_on=asset_list.landlord_property_id,
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right_on=asset_list.STANDARD_LANDLORD_PROPERTY_ID
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)
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rdf_with_solar["cavity_reason"] = rdf_with_solar["cavity_reason"].fillna("Not identified")
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rdf_with_solar["cavity_reason"].value_counts()
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# All others identified - some flagged as empties due to EPC or landlord data suggesting as much
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# 5 not identified due to containing COMPACTED BEAD
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asset_list.standardised_asset_list = asset_list.standardised_asset_list[
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asset_list.standardised_asset_list[asset_list.landlord_property_id]
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]
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z[asset_list.ATTRIBUTE_HAS_SOLAR].value_counts()
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pd.set_option('display.max_columns', None)
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z[[asset_list.STANDARD_FULL_ADDRESS, asset_list.STANDARD_POSTCODE, asset_list.ATTRIBUTE_HAS_SOLAR]]
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not_flagged = asset_list.standardised_asset_list[
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pd.isnull(asset_list.standardised_asset_list["solar_reason"])
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]
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# For everything not flagged for solar, identify why
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reasons = []
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for _, x in not_flagged.iterrows():
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if x[asset_list.STANDARD_PROPERTY_TYPE] == "flat":
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reason = "property is a flat"
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else:
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x[asset_list.EPC_API_DATA_NAMES["mainheat-description"]]
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reasons.append(
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{
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asset_list.DOMNA_PROPERTY_ID: x["asset_list.DOMNA_PROPERTY_ID"],
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"solar_exclusion_reason": reason,
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}
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)
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asset_list.load_contact_details(
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local_filepath=os.path.join(data_folder, "Full property list wth D&V report V look up 12.2.25.xlsx"),
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@ -462,7 +462,7 @@ class Property:
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if self.simulation_epcs is None:
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raise ValueError("Simulation EPCs have not been created")
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rec_ids = sorted(list(self.simulation_epcs.keys()))
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rec_ids = list(self.simulation_epcs.keys())
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updated_simulation_epcs = []
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for rec_id in rec_ids:
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sim_epc = self.simulation_epcs[rec_id].copy()
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@ -488,8 +488,6 @@ class Property:
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# Now we havet this data inthe
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self.updated_simulation_epcs = updated_simulation_epcs
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return updated_simulation_epcs
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@staticmethod
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def create_recommendation_scoring_data(
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property_id,
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@ -78,7 +78,7 @@ def app():
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# Set the inputs:
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portfolio_id = 139
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scenario_ids = [233, 234]
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scenario_ids = [237, 238]
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properties_data, plans_data, recommendations_data = get_data(
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portfolio_id=portfolio_id, scenario_ids=scenario_ids
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@ -299,6 +299,9 @@ def app():
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[
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"property_id", "uprn", "address", "postcode", "property_type", "walls", "roof", "heating", "windows",
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"current_epc_rating", "current_sap_points", "total_floor_area", "number_of_rooms",
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"co2_emissions", "current_energy_demand", "current_energy_demand_heating_hotwater",
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"heating_cost_current", "hot_water_cost_current", "lighting_cost_current",
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"appliances_cost_current", "gas_standing_charge", "electricity_standing_charge"
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]
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].merge(
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recommendations_measures_pivot, how="left", on="property_id"
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@ -306,6 +309,11 @@ def app():
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aggregated_metrics, how="left", on="property_id"
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)
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df["bills_total_cost"] = (
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df["heating_cost_current"] + df["hot_water_cost_current"] + df["lighting_cost_current"] +
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df["appliances_cost_current"] + df["gas_standing_charge"] + df["electricity_standing_charge"]
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)
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df = df.drop(columns=["property_id"])
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for c in ["sap_points", "co2_equivalent_savings", "energy_cost_savings", "kwh_savings"]:
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df[c] = df[c].fillna(0)
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@ -332,6 +340,11 @@ def app():
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df["Predicted Post Works SAP"] = df["Predicted Post Works SAP"].round()
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df["Predicted Post Works EPC"] = df["Predicted Post Works SAP"].apply(lambda x: sap_to_epc(x))
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# Calculate the relative savings on carbon, kwh, and bills
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df["relative_carbon_savings"] = df["co2_equivalent_savings"] / df["co2_emissions"]
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df["relative_kwh_savings"] = df["kwh_savings"] / df["current_energy_demand"]
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df["relative_bill_savings"] = df["energy_cost_savings"] / df["bills_total_cost"]
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# For properties that don't make it to EPC B, check why. E.g. for a property that has an oil boiler, it
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# the bills go up recommending HHRSH, so it doesn't make it to EPC B
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# For mid-terrace units, use the ordnance survey API to check if there is space for a heat pump?
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@ -360,13 +373,47 @@ def app():
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scenario_data[scenario] = df
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measure_counts = {}
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for scenario in scenario_ids:
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recommendation_cols = [c for c in scenario_data[scenario].columns if "Recommendation:" in c]
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measure_counts[scenario] = scenario_data[scenario][recommendation_cols].sum().to_dict()
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printing_scenario_id = scenario_ids[0]
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# EPC breakdown
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print(scenario_data[printing_scenario_id]['Predicted Post Works EPC'].value_counts())
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# Cost
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# Total cost
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print(scenario_data[printing_scenario_id]["total_cost"].sum())
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# Base cost
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print(scenario_data[printing_scenario_id]["estimated_cost"].sum())
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# Contingency
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print(scenario_data[printing_scenario_id]["contingency"].sum())
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# Costs averaged per unit
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print(scenario_data[printing_scenario_id]["total_cost"].mean())
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print(scenario_data[printing_scenario_id]["estimated_cost"].mean())
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print(scenario_data[printing_scenario_id]["contingency"].mean())
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pprint(measure_counts[scenario_ids[0]])
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pprint(measure_counts[scenario_ids[1]])
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# Average relative savings
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print(scenario_data[printing_scenario_id]["relative_carbon_savings"].mean())
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print(scenario_data[printing_scenario_id]["relative_kwh_savings"].mean())
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print(scenario_data[printing_scenario_id]["relative_bill_savings"].mean())
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measure_details = {}
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for scenario in scenario_ids:
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measure_details[scenario] = {}
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recommendation_cols = [c for c in scenario_data[scenario].columns if "Recommendation:" in c]
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measure_details[scenario]["count"] = scenario_data[scenario][recommendation_cols].sum().to_dict()
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# Get average cost per measure
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measure_columns = [
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c.split("Recommendation: ")[1] for c in scenario_data[scenario].columns if "Recommendation:" in c
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]
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# Take the mean, drop zero columns
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measure_costs = {}
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for m in measure_columns:
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measure_costs[m] = float(scenario_data[scenario][scenario_data[scenario][m] > 0][m].mean())
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measure_details[scenario]["cost_per_measure"] = measure_costs
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pprint(measure_details[scenario_ids[0]]["count"])
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pprint(measure_details[scenario_ids[1]]["count"])
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# Cost per measures
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pprint(measure_details[scenario_ids[0]]["cost_per_measure"])
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pprint(measure_details[scenario_ids[1]]["cost_per_measure"])
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# Do not get to EPC B:
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# 5 are flats
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@ -392,13 +439,20 @@ def app():
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scenario_metrics = {}
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for scenario in scenario_ids:
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df = scenario_data[scenario].copy()
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df["cost_per_sap_point"] = df["total_cost"] / df["sap_points"]
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df["cost_per_carbon"] = df["total_cost"] / df["co2_equivalent_savings"]
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avg_savings = df[
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["sap_points", "co2_equivalent_savings", "energy_cost_savings", "kwh_savings", "estimated_cost",
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"cost_per_sap_point", "cost_per_carbon", "total_cost", "contingency"]
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"total_cost", "contingency"]
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].mean().to_dict()
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avg_savings["cost_per_sap_point"] = avg_savings["total_cost"] / avg_savings["sap_points"]
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avg_savings["cost_per_carbon"] = avg_savings["total_cost"] / avg_savings["co2_equivalent_savings"]
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scenario_metrics[scenario] = avg_savings
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pprint(scenario_metrics[scenario_ids[0]])
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pprint(scenario_metrics[scenario_ids[1]])
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# TODO: Add a slide on valuation improvement, on a sample of properties?
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# TODO: Read in costing data and breakdown
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zz = scenario_recommendations_df[scenario_recommendations_df["type"] == "mechanical_ventilation"]
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@ -104,7 +104,7 @@ INSTALLER_ASHP_COSTS = [
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BOILER_UPGRADE_SCHEME_ASHP_VALUE = 7500
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INSTALLER_SOLAR_BATTERY_COSTS = [
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{'capacity_kwh': 5, 'description': 'Battery Add on', 'cost': 2030.40, 'installer': 'CEG'},
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{'capacity_kwh': 5, 'description': 'Battery Add on', 'cost': 3769.89, 'installer': 'JJC'},
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# {'capacity_kwh': 10, 'description': 'Battery Add on', 'cost': 4300.00, 'installer': 'CEG'},
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# {'capacity_kwh': 5, 'description': 'Battery Retrofit existing system', 'cost': 4250.00, 'installer': 'CEG'},
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# {'capacity_kwh': 10, 'description': 'Battery Retrofit Existing system', 'cost': 5950.00, 'installer': 'CEG'}
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@ -193,6 +193,8 @@ class Costs:
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# fittings and trimming doors, as well as scope for damage to the existing wall during preparation.
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IWI_CONTINGENCY = 0.2
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# For air source heat pumps, we inflate the assume cost by quite a bit to account for design and installation
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ASHP_CONTINGENCY = 0.35
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# Where there is more uncertainty, a higher contingency rate is used
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HIGH_RISK_CONTINGENCY = 0.2
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# When there is less uncertainty, a lower contingency rate is used
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@ -1168,7 +1170,7 @@ class Costs:
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cost = [x for x in INSTALLER_ASHP_COSTS if x][0]["cost"]
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# We add some contingency since there are additional costs such as resizing radiators, that could be required
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subtotal = cost * (1 + self.HIGH_RISK_CONTINGENCY)
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subtotal = cost * (1 + self.ASHP_CONTINGENCY)
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# The costs from installers exclude VAT
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vat = subtotal * self.VAT_RATE
|
||||
total_cost = subtotal + vat
|
||||
|
|
|
|||
|
|
@ -793,13 +793,26 @@ class Recommendations:
|
|||
]
|
||||
).sort_values(["phase", "recommendation_id"], ascending=True).reset_index(drop=True)
|
||||
|
||||
# We need the recommendaion type
|
||||
rec_id_to_type = {
|
||||
rec["recommendation_id"]: rec["type"] for recs in property_recommendations for rec in recs
|
||||
}
|
||||
rec_id_to_type[STARTING_DUMMY_ID_VALUE] = "starting_dummy"
|
||||
|
||||
for i in range(0, len(kwh_impact_table)):
|
||||
current_phase = kwh_impact_table.loc[i, 'phase']
|
||||
current = kwh_impact_table.loc[i]
|
||||
current_phase = current['phase']
|
||||
previous_phase_id = (current_phase - 1) if (current_phase > 0) else -9999
|
||||
previous_phase = kwh_impact_table[kwh_impact_table['phase'] == previous_phase_id]
|
||||
|
||||
if not previous_phase.empty:
|
||||
for col in ["predictions_heating", "predictions_hotwater"]:
|
||||
# Check if the recommendation type is ventilation
|
||||
if rec_id_to_type[current["recommendation_id"]] == "mechanical_ventilation":
|
||||
# We expect the kwh to increase
|
||||
if kwh_impact_table.loc[i, col] > previous_phase[col].max():
|
||||
continue
|
||||
|
||||
if kwh_impact_table.loc[i, col] > previous_phase[col].max():
|
||||
kwh_impact_table.loc[i, col] = previous_phase[col].max()
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue