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working on slides, debugging recommendations
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Khalim Conn-Kowlessar
parent
7e973d7955
commit
6e802b1f58
5 changed files with 240 additions and 15 deletions
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@ -1,6 +1,6 @@
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# Assumes that the average efficiency of an air source heat pump is 250%, taking the median of the 200-400% range,
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# which is often quoted as a sensible efficiency range for air source heat pumps.
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PESSIMISTIC_ASHPY_EFFICIENCY = 200
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PESSIMISTIC_ASHP_EFFICIENCY = 200
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AVERAGE_ASHP_EFFICIENCY = 300
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# Conservative estimate of the proportion of electricity that will be consumed, whereas the rest will
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@ -269,10 +269,13 @@ def make_asset_list():
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# We add a patch to one of the units because there's no data for the built form
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# We would be able to handle this automatically in the future, when using OS API
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patches = [{
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"uprn": "10033266220",
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"built-form": "Semi-Detached",
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}]
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patches = [
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{
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"uprn": "10033266220",
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"built-form": "Semi-Detached",
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},
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{'uprn': '10033266219', 'built-form': 'Semi-Detached'}
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]
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# Store patches in s3
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patches_filename = f"{USER_ID}/{PORTFOLIO_ID}/patches.json"
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214
etl/customers/newhaven/slides.py
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214
etl/customers/newhaven/slides.py
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@ -0,0 +1,214 @@
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import pandas as pd
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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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def get_data(portfolio_id, scenario_ids):
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session = sessionmaker(bind=db_engine)()
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session.begin()
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# Get properties and their details for a specific portfolio
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properties_query = session.query(
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PropertyModel,
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PropertyDetailsEpcModel
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).join(
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PropertyDetailsEpcModel, PropertyModel.id == PropertyDetailsEpcModel.property_id
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).filter(
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PropertyModel.portfolio_id == portfolio_id # Filter by portfolio ID
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).all()
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# Transform properties data to include all fields dynamically
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properties_data = [
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{**{col.name: getattr(prop.PropertyModel, col.name) for col in PropertyModel.__table__.columns},
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**{col.name: getattr(prop.PropertyDetailsEpcModel, col.name) for col in
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PropertyDetailsEpcModel.__table__.columns}}
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for prop in properties_query
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]
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# Get property IDs from fetched properties
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# Get plans linked to the fetched properties
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plans_query = session.query(Plan).filter(Plan.scenario_id.in_(scenario_ids)).all()
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# Transform plans data to include all fields dynamically
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plans_data = [
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{col.name: getattr(plan, col.name) for col in Plan.__table__.columns}
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for plan in plans_query
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]
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# Extract plan IDs for filtering recommendations through PlanRecommendations
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plan_ids = [plan['id'] for plan in plans_data]
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# Get recommendations through PlanRecommendations for those plans and that are default
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recommendations_query = session.query(
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Recommendation,
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Plan.scenario_id
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).join(
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PlanRecommendations, Recommendation.id == PlanRecommendations.recommendation_id
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).join(
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Plan, Plan.id == PlanRecommendations.plan_id # Join with Plan to access scenario_id
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).filter(
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PlanRecommendations.plan_id.in_(plan_ids),
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Recommendation.default == True # Filtering for default recommendations
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).all()
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# Transform recommendations data to include all fields dynamically and include scenario_id
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recommendations_data = [
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{**{col.name: getattr(rec.Recommendation, col.name) if hasattr(rec, 'Recommendation') else getattr(rec,
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col.name) for
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col in Recommendation.__table__.columns},
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"Scenario ID": rec.scenario_id}
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for rec in recommendations_query
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]
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session.close()
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return properties_data, plans_data, recommendations_data
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def slides():
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# Prepares the information required for the slides
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# Right now this is the second version of the nehaven portfolio
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portfolio_id = 90
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# Look at one scenario at a time, otherwise this is agony
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scenario_ids = [47, 48, 49]
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properties_data, plans_data, recommendations_data = get_data(portfolio_id, scenario_ids)
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properties_df = pd.DataFrame(properties_data)
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plans_df = pd.DataFrame(plans_data)
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recommendations_df = pd.DataFrame(recommendations_data)
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if properties_df.shape[0] != 2553:
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raise ValueError("The number of unique properties is not 2553")
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def estimate_post_retrofit_heating_hotwater_kwh(recommendations_df, scenario_ids):
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# Get the recommendations for the scenario, default
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scenario_comparison_df = []
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scenario_comparison_df_2 = []
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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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if scenario_recommendations['solar_kwh'].sum() > 0:
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blah
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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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grouped_data = scenario_recommendations.groupby(['property_id']).agg({
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'Estimated Kwh Savings': 'sum',
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'ligting_kwh': 'sum',
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'solar_kwh': 'sum'
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}).reset_index()
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comparison = properties_df.drop_duplicates().merge(
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grouped_data, on=["property_id"], how="left"
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)
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comparison["Post Retrofit Heating & Hotwater kwh"] = (
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comparison["current_energy_demand_heating_hotwater"] - \
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comparison["Estimated Kwh Savings"]
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)
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avgs = comparison[['current_energy_demand_heating_hotwater', 'Post Retrofit Heating & Hotwater kwh']].mean()
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# We now, for properties that have a plan, do a before and after
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with_savings = comparison[~pd.isnull(comparison["Estimated Kwh Savings"])]
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avgs2 = with_savings[
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['current_energy_demand_heating_hotwater', 'Post Retrofit Heating & Hotwater kwh']].mean()
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avgs2["difference"] = avgs2["current_energy_demand_heating_hotwater"] - avgs2[
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"Post Retrofit Heating & Hotwater kwh"]
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avgs2["percentage_reduction"] = 100 * avgs2["difference"] / avgs2["current_energy_demand_heating_hotwater"]
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scenario_comparison_df.append({"scenario_id": scenario_id, **avgs})
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scenario_comparison_df_2.append({"scenario_id": scenario_id, **avgs2})
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scenario_comparison_df = pd.DataFrame(scenario_comparison_df)
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scenario_comparison_df_2 = pd.DataFrame(scenario_comparison_df_2)
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return scenario_comparison_df, scenario_comparison_df_2
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# TODO: How do we factor in solar PV
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# Q1: What is the baseline heating and energy demand for the properties in the portfolio - baseline?
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heating_hotwater_kwh = (
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properties_df[['current_energy_demand', 'current_energy_demand_heating_hotwater']]
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.mean()
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)
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# Q2: For each scenario, what is the £ per kwh reduction?
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# Calculate total kwh savings
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kwh_plan_impact = estimate_post_retrofit_heating_hotwater_kwh(properties_df, recommendations_df)
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z = df[
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(df["Recommendation Default Status"] == True) &
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(df["Plan Name"].isin(['Demand Reduction – cavity & roof insulation']))
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]
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z2 = z[z["Property ID"] == 25215]
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# Find duplicated property ID, recommendationt type combos
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z = z[z.duplicated(subset=["Property ID", "Recommendation Type"])]
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for plan_name in df["Plan Name"].unique():
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# Get default recs
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default_recs = df[
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(df["Recommendation Default Status"] == True) &
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(df["Plan Name"] == plan_name)
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].copy()
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if default_recs["Recommendation ID"].duplicated().sum():
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raise Exception("somethign went wrong")
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default_recs["Recommendation Type"].unique()
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# We now calculate the total savings
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total_savings = default_recs["Estimated Kwh Savings"].sum()
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total_cost = default_recs["Recommendation Cost"].sum()
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kwh_savings = df[
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df["Recommendation Default Status"] == True
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].groupby("Plan Name")[["Estimated Kwh Savings", "Recommendation Cost"]].sum().rename(
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columns={"Estimated Kwh Savings": "Total Kwh Savings", "Recommendation Cost": "Total Cost"}
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).reset_index()
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kwh_savings["Cost per Kwh Saved"] = kwh_savings["Total Cost"] / kwh_savings["Total Kwh Savings"]
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# Q3: For each scenario, we want to answer what the heating and hot water kwh looks like after retrofit
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# We need to take recommndations that affect just the heating and hot water
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# By property
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df["Type Mapped"] = df["Recommendation Type"].copy().replace(
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{
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"loft_insulation": "roof_insulation",
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"room_roof_insulation": "roof_insulation",
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"flat_roof_insulation": "roof_insulation",
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"hot_water_tank_insulation": "other",
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"cylinder_thermostat": "other",
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"sealing_open_fireplace": "other",
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}
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)
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# Group by 'Plan Name' and 'Recommendation Type' and count unique 'Property ID'
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recommendation_summary = df.groupby(['Plan Name', 'Type Mapped']).agg({
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'Property ID': 'nunique'
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}).reset_index()
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recommendation_summary.columns = ['Plan Name', 'Type Mapped', 'Number of Properties']
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recommendation_summary["Percentage of Properties"] = 100 * (
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recommendation_summary["Number of Properties"] / df["Property ID"].nunique()
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)
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@ -74,7 +74,6 @@ class FloorRecommendations(Definitions):
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u_value = self.property.floor["thermal_transmittance"]
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property_type = self.property.data["property-type"]
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floor_area = self.property.insulation_floor_area
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year_built = self.property.year_built
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if self.property.floor["another_property_below"] | (self.property.floor["insulation_thickness"] in [
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"average", "above average"
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@ -95,14 +94,16 @@ class FloorRecommendations(Definitions):
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if u_value:
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# By being built more recently than this, it means that the property was likely build with soild
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# concrete floors with insulation already
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if year_built < self.PART_L_YEAR_CUTOFF:
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raise NotImplementedError("Not investigated this use case")
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if u_value <= self.BUILDING_REGULATIONS_PART_L_MAX_U_VALUE:
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# The floor is already compliant
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return
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# In this case where we have the u-value of a floor, we likely don't have any other information about it
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# so there is no recommendation that we can practically make
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if (
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self.property.floor["is_suspended"] or
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self.property.floor["is_to_unheated_space"] or
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self.property.floor["is_to_external_air"] or
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self.property.floor["is_solid"]
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):
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raise ValueError("This should not be possible")
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return
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if u_value is None:
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u_value = get_floor_u_value(
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@ -20,7 +20,9 @@ import backend.app.assumptions as assumptions
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ASHP_COP = 3
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DESCRIPTIONS_TO_FUEL_TYPES = {
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"Air source heat pump, radiators, electric": {"fuel": "Electricity", "cop": ASHP_COP},
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"Air source heat pump, radiators, electric": {
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"fuel": "Electricity", "cop": assumptions.AVERAGE_ASHP_EFFICIENCY / 100
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},
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"Boiler and radiators, mains gas": {"fuel": 'Natural Gas', "cop": 0.9},
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'Electric storage heaters': {"fuel": 'Electricity', "cop": 1},
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"Electric immersion, off-peak": {"fuel": 'Electricity', "cop": 1},
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@ -46,6 +48,11 @@ DESCRIPTIONS_TO_FUEL_TYPES = {
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"Boiler and radiators, dual fuel (mineral and wood)": {"fuel": "Wood Logs", "cop": 0.9},
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"Electric immersion, standard tariff, plus solar": {"fuel": "Electricity + Solar Thermal", "cop": 1},
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"From main system, flue gas heat recovery": {"fuel": "Natural Gas", "cop": 0.9},
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"Electric underfloor heating": {"fuel": "Electricity", "cop": 1},
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"No system present: electric immersion assumed": {"fuel": "Electricity", "cop": 1},
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"Air source heat pump, underfloor, electric": {
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"fuel": "Electricity", "cop": assumptions.AVERAGE_ASHP_EFFICIENCY / 100
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},
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}
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STARTING_DUMMY_ID_VALUE = -9999
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