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rough analysis for funding eligibility
This commit is contained in:
Khalim Conn-Kowlessar
parent
5316477b94
commit
ceb34979e4
4 changed files with 504 additions and 1 deletions
97
etl/customers/Cleethorpes Portfolio/epc data.py
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97
etl/customers/Cleethorpes Portfolio/epc data.py
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@ -0,0 +1,97 @@
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import os
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import pandas as pd
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from backend.SearchEpc import SearchEpc
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from dotenv import load_dotenv
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from tqdm import tqdm
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load_dotenv(dotenv_path="backend/.env")
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EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN")
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def app():
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"""
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Simple script to pull the EPC data for the Cleethorpes Portfolio
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:return:
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"""
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asset_list = pd.read_excel(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/sfr/Cleethorpes Portoflio/Updated Tenancy Schedule "
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"Portfolio.xlsx",
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)
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asset_list["row_id"] = asset_list.index
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asset_list[" Street No."] = asset_list[" Street No."].astype(str)
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epc_data = []
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for _, property in tqdm(asset_list.iterrows(), total=len(asset_list)):
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if property[" Street No."] == "Ground Floor Commercial":
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continue
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uprn = property["Uprn"]
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if not pd.isnull(uprn):
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searcher = SearchEpc(
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address1="",
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postcode="",
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auth_token=EPC_AUTH_TOKEN,
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os_api_key="",
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uprn=int(uprn)
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)
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searcher.find_property(skip_os=True)
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else:
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if not pd.isnull(property[" Flat No."]) and property[" Flat No."] not in ["", " "]:
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address1 = property[" Flat No."].strip() + ", " + property[" Street No."].strip()
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else:
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address1 = property[" Street No."].strip()
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if address1 == "1a Mews House 30":
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address1 = "1a Rear of"
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searcher = SearchEpc(
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address1=address1,
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postcode=property[" Postcode"].strip(),
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auth_token=EPC_AUTH_TOKEN,
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os_api_key="",
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uprn=None,
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)
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searcher.get_epc()
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# Get the newest record on lodgement-date
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sorted_epcs = sorted(
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searcher.data["rows"], key=lambda x: x["lodgement-date"]
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)
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searcher.newest_epc = sorted_epcs[-1]
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if searcher.newest_epc is None:
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raise ValueError(f"No EPC found for UPRN: {uprn}")
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epc_data.append(
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{
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"row_id": property["row_id"],
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**searcher.newest_epc
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}
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)
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epc_df = pd.DataFrame(epc_data)
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# Merge on data
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asset_list_with_epc = asset_list.merge(
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epc_df[["row_id", "address", "current-energy-rating", "current-energy-efficiency", "lodgement-date"]],
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how="left",
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left_on="row_id",
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right_on="row_id",
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).rename(
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columns={
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"address": "EPC Address",
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"current-energy-rating": "Current EPC Rating",
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"current-energy-efficiency": "Current SAP Score",
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"lodgement-date": "EPC Date"
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}
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)
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asset_list_with_epc.to_excel(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/sfr/Cleethorpes Portoflio/Portfolio with EPCs.xlsx",
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index=False
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)
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epc_df.to_csv(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/sfr/Cleethorpes Portoflio/epc_data.csv",
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index=False
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)
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@ -102,7 +102,7 @@ analysis_epcs = analysis_epcs[
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[
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"UPRN", "TENURE", "CURRENT_ENERGY_RATING", "WALLS_DESCRIPTION", "ROOF_DESCRIPTION",
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"CONSTRUCTION_AGE_BAND", "TOTAL_FLOOR_AREA", "PROPERTY_TYPE", "BUILT_FORM", "MAINHEAT_DESCRIPTION",
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"eligibility_type",
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"eligibility_type", "PHOTO_SUPPLY", "ADDRESS1", "POSTCODE"
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]
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]
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analysis_epcs["grouped_epc_band"] = np.where(
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@ -110,6 +110,14 @@ analysis_epcs["grouped_epc_band"] = np.where(
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"EPC D",
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"EPC E-G"
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)
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analysis_epcs[pd.isnull(analysis_epcs["PHOTO_SUPPLY"])][["ADDRESS1", "POSTCODE"]].sample(1)
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analysis_epcs["PHOTO_SUPPLY"] = analysis_epcs["PHOTO_SUPPLY"].fillna(0)
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analysis_epcs["PHOTO_SUPPLY"] = analysis_epcs["PHOTO_SUPPLY"].astype(float)
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analysis_epcs["has_solar"] = np.where(analysis_epcs["PHOTO_SUPPLY"] > 0, 1, 0)
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analysis_epcs["has_solar"].value_counts()
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analysis_epcs.to_csv("/Users/khalimconn-kowlessar/Documents/hestia/Customers/bcc tender/analysis_epcs.csv", index=False)
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# Create aggregations and we store this information
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@ -5,6 +5,7 @@ from sqlalchemy.orm import sessionmaker
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from backend.app.db.connection import db_engine
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from backend.app.db.models.recommendations import Recommendation, Plan, PlanRecommendations, Scenario
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from backend.app.db.models.portfolio import PropertyModel, PropertyDetailsEpcModel
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from utils.s3 import read_csv_from_s3
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def get_data(portfolio_id, scenario_ids):
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@ -415,3 +416,241 @@ def slides():
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pd.set_option('display.max_rows', None)
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# Show more characters in a column
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pd.set_option('display.max_colwidth', None)
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# preparing of this data for the following 2 needs:
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# 1) dataset to share with Nextgen heating
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# 2) Breakdown of results by property type
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# get the asset list
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asset_list = read_csv_from_s3(bucket_name="retrofit-plan-inputs-dev", filepath="8/90/pilot.csv")
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asset_list = pd.DataFrame(asset_list)
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# Get non-invasive recommendations
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non_intrusive_recommendations = read_csv_from_s3(
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bucket_name="retrofit-plan-inputs-dev",
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filepath="8/90/non_invasive_recommendations.csv"
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)
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non_intrusive_recommendations = pd.DataFrame(non_intrusive_recommendations)
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# Unnest this
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import ast
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survey_recs = []
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for _, row in non_intrusive_recommendations.iterrows():
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recs = ast.literal_eval(row["recommendations"])
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ashp_rec = next((r for r in recs if r["type"] == "air_source_heat_pump"), None)
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solar_rec = next((r for r in recs if r["type"] == "solar_pv"), None)
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to_append = {
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"uprn": row["uprn"]
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}
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if ashp_rec["suitable"]:
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to_append = {
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**to_append,
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"ashp_suitable": True,
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"ashp_size_kw": ashp_rec["size"],
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"ashp_cost": ashp_rec["cost"],
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}
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if solar_rec["suitable"]:
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to_append = {
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**to_append,
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"solar_suitable": True,
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"solar_size_kwp": solar_rec["array_wattage"],
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"solar_cost": solar_rec["cost"],
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}
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survey_recs.append(to_append)
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survey_recs = pd.DataFrame(survey_recs)
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asset_list["uprn"] = asset_list["uprn"].astype(int)
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survey_recs["uprn"] = survey_recs["uprn"].astype(int)
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vital_kwh = 7597
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domna_kwh = 10850
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scaling_factor = vital_kwh / domna_kwh
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next_gen_dataset = properties_df[[
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"uprn", "address", "postcode",
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"property_type", "built_form", "current_energy_demand_heating_hotwater",
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"mainfuel", "total_floor_area", "floor_height"
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]].rename(
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columns={
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"mainfuel": "primary_fuel_type",
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"total_floor_area": "gross_floor_area",
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"current_energy_demand_heating_hotwater": "estimated_heating_hotwater_kwh"
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}
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).merge(
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asset_list[["uprn", "number_of_floors"]],
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how="left",
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on="uprn"
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).merge(
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survey_recs,
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how="left",
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on="uprn"
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)
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next_gen_dataset["estimated_heating_hotwater_kwh_scaled"] = (
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next_gen_dataset["estimated_heating_hotwater_kwh"] * scaling_factor
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)
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next_gen_dataset["ashp_suitable"] = next_gen_dataset["ashp_suitable"].fillna(False)
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next_gen_dataset["solar_suitable"] = next_gen_dataset["solar_suitable"].fillna(False)
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# We prepare the scenario outputs by property type
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grouped_data = next_gen_dataset.copy()
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grouped_data["property_sub_type"] = grouped_data["built_form"].copy()
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# If a property is a flat, re-map sub_type just to flat
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grouped_data.loc[grouped_data["property_type"] == "Flat", "property_sub_type"] = "Flat"
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# Same for maisonettes
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grouped_data.loc[grouped_data["property_type"] == "Maisonette", "property_sub_type"] = "Maisonette"
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# We now pull out the recommendations impact by property type and sub type
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property_scenario_impact = []
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for scenario_id in scenario_ids:
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# Get the recommendations for the scenario, default
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scenario_recommendations = recommendations_df[
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(recommendations_df["Scenario ID"] == scenario_id) &
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(recommendations_df["default"] == True)
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].copy()
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scenario_recommendations['ligting_kwh'] = scenario_recommendations.apply(
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lambda x: x['kwh_savings'] if x['type'] == 'low_energy_lighting' else 0,
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axis=1)
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scenario_recommendations['solar_kwh'] = scenario_recommendations.apply(
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lambda x: x['kwh_savings'] if x['type'] == 'solar_pv' else 0, axis=1)
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# Set 'Estimated Kwh Savings' to zero where specific kwh columns are used
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scenario_recommendations['Estimated Kwh Savings'] = scenario_recommendations.apply(
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lambda x: 0 if x['type'] in ['low_energy_lighting', 'solar_pv'] else x[
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'kwh_savings'], axis=1)
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scenario_grouped_data = scenario_recommendations.groupby(['property_id']).agg({
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'Estimated Kwh Savings': 'sum',
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"estimated_cost": "sum"
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}).reset_index()
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comparison = properties_df.drop_duplicates()[
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["uprn", "property_id", "current_energy_demand_heating_hotwater"]
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].merge(
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scenario_grouped_data, on=["property_id"], how="left"
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)
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comparison["Estimated Kwh Savings"] = comparison["Estimated Kwh Savings"].fillna(0)
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comparison["estimated_cost"] = comparison["estimated_cost"].fillna(0)
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comparison["post_scenario_heating_hotwater_kwh"] = (
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comparison["current_energy_demand_heating_hotwater"] - comparison["Estimated Kwh Savings"]
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)
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comparison["scenario_id"] = scenario_id
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property_scenario_impact.append(comparison)
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property_scenario_impact = pd.concat(property_scenario_impact)
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property_scenario_impact = property_scenario_impact.drop(columns=["property_id", "Estimated Kwh Savings"])
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# Scale
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property_scenario_impact["post_scenario_heating_hotwater_kwh_scaled"] = (
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property_scenario_impact["post_scenario_heating_hotwater_kwh"] * scaling_factor
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)
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grouped_data = grouped_data.merge(
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property_scenario_impact, how="left", on="uprn"
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)
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# Agg the data
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grouped_data = grouped_data.groupby(["property_type", "property_sub_type", "scenario_id"]).agg({
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"estimated_heating_hotwater_kwh": "mean",
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"estimated_heating_hotwater_kwh_scaled": "mean",
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"estimated_cost": "mean",
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"post_scenario_heating_hotwater_kwh": "mean",
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"post_scenario_heating_hotwater_kwh_scaled": "mean"
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}).reset_index()
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scenario_names = pd.DataFrame(
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[
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{
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"scenario_id": 47,
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"scenario": "Demand Reduction – cavity & roof insulation",
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},
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{
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"scenario_id": 48,
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"scenario": "Demand reduction – no solid wall, floors or heating/renewables",
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},
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{
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"scenario_id": 49,
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"scenario": "Demand reduction – no decant"
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},
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{
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"scenario_id": 50,
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"scenario": "Demand reduction – no decant + heating & solar",
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},
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{
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"scenario_id": 51,
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"scenario": "Whole house retrofit"
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}
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]
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)
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grouped_data = grouped_data.merge(
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scenario_names, how="left", on="scenario_id"
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)
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if not grouped_data[
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grouped_data["estimated_heating_hotwater_kwh"] < grouped_data["post_scenario_heating_hotwater_kwh"]].empty:
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raise Exception("someting went wrong")
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if not grouped_data[grouped_data["estimated_heating_hotwater_kwh_scaled"] < grouped_data[
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"post_scenario_heating_hotwater_kwh_scaled"]].empty:
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raise Exception("someting went wrong")
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# Reorder the columns
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grouped_data = grouped_data[
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[
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'property_type',
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'property_sub_type',
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'scenario',
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'estimated_heating_hotwater_kwh',
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'post_scenario_heating_hotwater_kwh',
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'estimated_heating_hotwater_kwh_scaled',
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'post_scenario_heating_hotwater_kwh_scaled',
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'estimated_cost',
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]
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]
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grouped_data = grouped_data.rename(
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columns={
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"property_type": "Property Type",
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"property_sub_type": "Property Sub Type",
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"scenario": "Scenario",
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"estimated_heating_hotwater_kwh": "Estimated Heating & Hot Water kwh",
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"post_scenario_heating_hotwater_kwh": "Post Scenario Heating & Hot Water kwh",
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"estimated_heating_hotwater_kwh_scaled": "Estimated Heating & Hot Water kwh (scaled)",
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"post_scenario_heating_hotwater_kwh_scaled": "Post Scenario Heating & Hot Water kwh (scaled)",
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"estimated_cost": "Estimated Cost or Retrofit",
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}
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)
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grouped_data.to_excel(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Newhaven/outputs/Scenario kWh Impact by Property "
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"Type.xlsx",
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index=False
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)
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property_scenario_impact = property_scenario_impact.merge(
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scenario_names, how="left", on="scenario_id"
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)
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df_pivot = property_scenario_impact.pivot_table(index='uprn', columns='scenario',
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values=['post_scenario_heating_hotwater_kwh',
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'post_scenario_heating_hotwater_kwh_scaled'])
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# Flattening multi-index columns
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df_pivot.columns = [f'{col[0]}_{col[1]}' for col in df_pivot.columns]
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# Reset the index to have a clean dataframe
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df_pivot.reset_index(inplace=True)
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next_gen_dataset = next_gen_dataset.merge(
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df_pivot, how="left", on="uprn"
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)
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next_gen_dataset.to_csv(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Newhaven/outputs/next_gen_dataset.csv", index=False
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)
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159
etl/sfr/midlands_portfolio_est_funding.py
Normal file
159
etl/sfr/midlands_portfolio_est_funding.py
Normal file
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@ -0,0 +1,159 @@
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import msgpack
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import pandas as pd
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from utils.s3 import read_from_s3
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from recommendations.recommendation_utils import (
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estimate_number_of_floors, esimtate_pitched_roof_area, estimate_external_wall_area, estimate_perimeter
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)
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def app():
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"""
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Aims to estimate the amount of GBIS funding eligible
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:return:
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"""
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cleaned = read_from_s3(
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s3_file_name="cleaned_epc_data/cleaned.bson",
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bucket_name="retrofit-data-dev"
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)
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cleaned = msgpack.unpackb(cleaned, raw=False)
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epc_data = pd.read_excel(
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"/Users/khalimconn-kowlessar/Downloads/20240820 portfolio_epc_data.xlsx"
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)
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# For simplicity, get roofs or cavities
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epc_data = epc_data.merge(
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pd.DataFrame(cleaned["roof-description"]),
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how="left",
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left_on="ROOF_DESCRIPTION",
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right_on="original_description"
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)
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epc_data["needs_roof_work"] = epc_data["insulation_thickness"].isin(
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[
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None,
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"100",
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'150',
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'50',
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'75',
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'below average',
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'25',
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'12'
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]
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) & (epc_data["is_flat"] | epc_data["is_pitched"])
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epc_data = epc_data.merge(
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pd.DataFrame(cleaned["walls-description"]),
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how="left",
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left_on="WALLS_DESCRIPTION",
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right_on="original_description",
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suffixes=("", "_wall")
|
||||
)
|
||||
|
||||
epc_data["needs_cavity_done"] = epc_data["is_cavity_wall"] & epc_data["insulation_thickness_wall"].isin(
|
||||
['none', "below average"]
|
||||
)
|
||||
|
||||
loft_insulation_per_m2 = 16.07
|
||||
flat_roof_insulation_per_m2 = 195
|
||||
cwi_per_m2 = 14.21
|
||||
gbis_abs = 30
|
||||
|
||||
# We assume the work will take the home from a high D to a low D
|
||||
def get_abs(floor_area):
|
||||
if floor_area <= 72:
|
||||
return 155
|
||||
|
||||
if floor_area <= 97:
|
||||
return 169
|
||||
|
||||
if floor_area <= 199:
|
||||
return 196.4
|
||||
|
||||
return 350.1
|
||||
|
||||
estimated_costs = []
|
||||
for _, home in epc_data.iterrows():
|
||||
to_append = {
|
||||
"uprn": home["UPRN"],
|
||||
"address": home["ADDRESS"],
|
||||
"postcode": home["POSTCODE"],
|
||||
}
|
||||
|
||||
project_abs = get_abs(home["TOTAL_FLOOR_AREA"])
|
||||
available_funding = project_abs * gbis_abs
|
||||
|
||||
n_floors = estimate_number_of_floors(home["PROPERTY_TYPE"])
|
||||
floor_height = float(home["FLOOR_HEIGHT"]) if not pd.isnull(home["FLOOR_HEIGHT"]) else 2.5
|
||||
|
||||
# Check if it needs the walls done
|
||||
if home["needs_cavity_done"]:
|
||||
# We estimate the amount of insulation required
|
||||
est_perimeter = estimate_perimeter(
|
||||
floor_area=float(home["TOTAL_FLOOR_AREA"]) / n_floors,
|
||||
num_rooms=float(home["NUMBER_HABITABLE_ROOMS"]) / n_floors
|
||||
)
|
||||
|
||||
insulation_needed = estimate_external_wall_area(
|
||||
num_floors=n_floors,
|
||||
floor_height=floor_height,
|
||||
perimeter=est_perimeter,
|
||||
built_form=home["BUILT_FORM"],
|
||||
)
|
||||
cost_of_insulation = insulation_needed * cwi_per_m2
|
||||
|
||||
if available_funding > cost_of_insulation:
|
||||
available_funding = cost_of_insulation
|
||||
|
||||
to_append = {
|
||||
**to_append,
|
||||
"available_funding": available_funding,
|
||||
"measure": "Cavity Wall Insulation",
|
||||
"project_abs": project_abs
|
||||
}
|
||||
|
||||
estimated_costs.append(to_append)
|
||||
continue
|
||||
|
||||
if home["needs_roof_work"]:
|
||||
# We estimate how much the cost of insulation would be
|
||||
if home["is_pitched"]:
|
||||
measure = "Loft Insulation"
|
||||
|
||||
roof_area = float(home["TOTAL_FLOOR_AREA"]) / n_floors
|
||||
cost_of_insulation = roof_area * loft_insulation_per_m2
|
||||
else:
|
||||
measure = "Flat Roof Insulation"
|
||||
roof_area = float(home["TOTAL_FLOOR_AREA"]) / n_floors
|
||||
cost_of_insulation = roof_area * flat_roof_insulation_per_m2
|
||||
|
||||
if available_funding > cost_of_insulation:
|
||||
available_funding = cost_of_insulation
|
||||
|
||||
to_append = {
|
||||
**to_append,
|
||||
"available_funding": available_funding,
|
||||
"measure": measure,
|
||||
"project_abs": project_abs
|
||||
}
|
||||
|
||||
estimated_costs.append(to_append)
|
||||
continue
|
||||
|
||||
estimated_costs = pd.DataFrame(estimated_costs)
|
||||
|
||||
estimated_costs.groupby("measure")["available_funding"].mean()
|
||||
estimated_costs["measure"].value_counts()
|
||||
|
||||
estimated_costs.to_csv("/Users/khalimconn-kowlessar/Documents/hestia/Customers/sfr/estimated_costs_gbis.csv")
|
||||
|
||||
epc_data[["UPRN", "ADDRESS", "POSTCODE"]].to_csv(
|
||||
"/Users/khalimconn-kowlessar/Documents/hestia/sfr/council_tax_bands_sample.csv")
|
||||
|
||||
n_properties_for_ashp = epc_data[
|
||||
(epc_data["PROPERTY_TYPE"] == "House") &
|
||||
(epc_data["BUILT_FORM"].isin(["Detached", "Semi-Detached"]))
|
||||
].shape[0]
|
||||
Loading…
Add table
Reference in a new issue