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preparing the data for lewes council
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Khalim Conn-Kowlessar
parent
d7ed4dd9a4
commit
681a449187
1 changed files with 192 additions and 57 deletions
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@ -417,9 +417,14 @@ def slides():
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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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def app():
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"""
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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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:return:
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"""
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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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@ -431,6 +436,14 @@ def slides():
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)
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non_intrusive_recommendations = pd.DataFrame(non_intrusive_recommendations)
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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, 50, 51]
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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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recommendations_df = pd.DataFrame(recommendations_data)
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# Unnest this
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import ast
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survey_recs = []
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@ -502,27 +515,74 @@ def slides():
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# We now pull out the recommendations impact by property type and sub type
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# Exclude sealing open fireplaces
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recommendations_df = recommendations_df[recommendations_df["type"] != "sealing_open_fireplace"]
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# We update the type column so that if type == heating, and the description contains "air source heat pump",
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# the type is "air_source_heat_pump", else if the description contains "high heat retention storage heaters", else
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# if the description contains "condensing boiler, the type is updated to "boiler_upgrade"
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recommendations_df["type"] = np.where(
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recommendations_df["type"] == "heating",
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np.where(
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recommendations_df["description"].str.contains("air source heat pump"),
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"air_source_heat_pump",
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np.where(
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recommendations_df["description"].str.contains("high heat retention"),
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"high_heat_retention_storage_heaters",
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np.where(
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recommendations_df["description"].str.contains("condensing boiler"),
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"boiler_upgrade",
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recommendations_df["type"]
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)
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)
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),
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recommendations_df["type"]
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)
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recommendation_types = recommendations_df["type"].unique().tolist()
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rename_dict = {
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'hot_water_tank_insulation': 'Hot Water Tank Insulation',
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'windows_glazing': 'Windows Glazing',
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'secondary_heating': 'Secondary Heating',
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'cavity_wall_insulation': 'Cavity Wall Insulation',
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'flat_roof_insulation': 'Flat Roof Insulation',
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'mechanical_ventilation': 'Mechanical Ventilation',
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'loft_insulation': 'Loft Insulation',
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'cylinder_thermostat': 'Cylinder Thermostat',
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'room_roof_insulation': 'Room Roof Insulation',
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'low_energy_lighting': 'Low Energy Lighting',
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'external_wall_insulation': 'External Wall Insulation',
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'heating': 'Heating',
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'solar_pv': 'Solar PV',
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'heating_control': 'Heating Control',
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'solid_floor_insulation': 'Solid Floor Insulation',
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'suspended_floor_insulation': 'Suspended Floor Insulation',
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'internal_wall_insulation': 'Internal Wall Insulation'
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}
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property_scenario_impact = []
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for scenario_id in scenario_ids:
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for scenario_id in tqdm(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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scenario_recommendations['Estimated Lighting kWh Savings'] = 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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scenario_recommendations['Estimated Solar kWh Savings'] = 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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scenario_recommendations['Estimated Heating Demand 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 Heating Demand kWh Savings': 'sum',
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'Estimated Lighting kWh Savings': 'sum',
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'Estimated Solar kWh Savings': 'sum',
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"estimated_cost": "sum"
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}).reset_index()
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@ -531,18 +591,52 @@ def slides():
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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 Heating Demand kWh Savings"] = (
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comparison["Estimated Heating Demand kWh Savings"].fillna(0)
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)
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comparison["Estimated Lighting kWh Savings"] = (
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comparison["Estimated Lighting kWh Savings"].fillna(0)
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)
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comparison["Estimated Solar kWh Savings"] = (
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comparison["Estimated Solar kWh Savings"].fillna(0)
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)
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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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comparison["current_energy_demand_heating_hotwater"] - comparison["Estimated Heating Demand kWh Savings"]
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)
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# For each scenario, we create a measure matrix
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measure_matrix = scenario_recommendations.pivot_table(
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index='property_id',
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columns='type',
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values='id', # Using 'id' just as a placeholder for the pivot
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aggfunc=lambda x: True, # If an ID exists for a given type, mark as True
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fill_value=False # Fill other entries as False
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).reset_index()
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non_zero_heat_demand_impact = comparison[
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(comparison["Estimated Heating Demand kWh Savings"] > 0) |
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(comparison["Estimated Lighting kWh Savings"] > 0) |
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(comparison["Estimated Solar kWh Savings"] > 0)
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]
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measure_matrix = measure_matrix[
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measure_matrix["property_id"].isin(non_zero_heat_demand_impact["property_id"].values)
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]
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measure_matrix = measure_matrix.rename(columns=rename_dict)
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comparison = comparison.merge(
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measure_matrix, on="property_id", how="left"
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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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# property_scenario_impact = property_scenario_impact.drop(columns=["property_id", "Estimated Kwh Savings"])
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for v in rename_dict.values():
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# Fill NaNs with False
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property_scenario_impact[v] = property_scenario_impact[v].fillna(False)
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# Scale
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property_scenario_impact["post_scenario_heating_hotwater_kwh_scaled"] = (
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@ -600,57 +694,98 @@ def slides():
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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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# 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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property_scenario_impact = property_scenario_impact.sort_values(
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["postcode", "uprn", "scenario_id"], ascending=True
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)
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lewes_data = next_gen_dataset.merge(
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property_scenario_impact, how="left", on="uprn"
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)
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# Rearrange, rename columns and drop what we don't need
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# TODO - remap the heating type
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lewes_data = lewes_data[
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[
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'uprn', 'address', 'postcode', 'property_type', 'built_form', 'estimated_heating_hotwater_kwh',
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'primary_fuel_type', 'gross_floor_area', 'floor_height', 'number_of_floors', 'ashp_suitable',
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'ashp_size_kw',
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'ashp_cost', 'solar_suitable', 'solar_size_kwp', 'solar_cost', 'estimated_heating_hotwater_kwh_scaled',
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# 'property_id', - dropped
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'current_energy_demand_heating_hotwater', 'Estimated Heating Demand kWh Savings',
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'Estimated Lighting kWh Savings', 'Estimated Solar kWh Savings', 'estimated_cost',
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'post_scenario_heating_hotwater_kwh', 'Cavity Wall Insulation', 'Cylinder Thermostat',
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'Flat Roof Insulation',
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'Hot Water Tank Insulation', 'Loft Insulation', 'Mechanical Ventilation', 'Room Roof Insulation',
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# 'scenario_id', - dropped
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'Low Energy Lighting', 'Secondary Heating', 'Windows Glazing', 'External Wall Insulation',
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'Heating',
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'Heating Control',
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'Solar PV',
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'Internal Wall Insulation',
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'Solid Floor Insulation',
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'Suspended Floor Insulation',
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'post_scenario_heating_hotwater_kwh_scaled',
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'scenario'
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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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]
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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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# We save this dataset, which will be shared with Lewes Council
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lewes_data.to_csv(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Newhaven/outputs/property data.csv", 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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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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# 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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# 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 = 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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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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