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Completed set up of bills costs
This commit is contained in:
Khalim Conn-Kowlessar
parent
5274b9b713
commit
4c79abe512
3 changed files with 115 additions and 44 deletions
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@ -643,14 +643,24 @@ class Property:
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https://www.sciencedirect.com/science/article/pii/S0378778823002542
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:return:
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"""
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# We should adjust the costs first and then calculate the energy consumption
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# These are the costs at the time that the EPC was created - we need a cost based on today's prices so
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# we do the following:
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# 1) Score the models to get kwh for heating and hot water
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# 2) Adjust the kwh values with the UCL paper
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# 3) Convert the kwh values to costs today using current prices
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#
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# For the moment, we'll just use the same cost for lighting, since the value is quite low
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# We get the following things:
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# 1) Today's cost. This give us a basline figure for what the cost is today
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# 2) Predicted KwH
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# Today's costs
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todays_heating_cost = energy_consumption_client.convert_cost_to_today(
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original_cost=float(self.data["heating-cost-current"]),
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lodgement_date=pd.Timestamp(self.epc_record.prepared_epc["lodgement_date"])
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)
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todays_hot_water_cost = energy_consumption_client.convert_cost_to_today(
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original_cost=float(self.data["hot-water-cost-current"]),
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lodgement_date=pd.Timestamp(self.epc_record.prepared_epc["lodgement_date"])
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)
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todays_lighting_cost = energy_consumption_client.convert_cost_to_today(
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original_cost=float(self.data["lighting-cost-current"]),
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lodgement_date=pd.Timestamp(self.epc_record.prepared_epc["lodgement_date"])
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)
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scoring_df = pd.DataFrame([self.epc_record.prepared_epc])
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# Change columns from underscores to hyphens
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@ -694,33 +704,34 @@ class Property:
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current_epc_rating=self.data["current-energy-rating"],
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)
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# Convert to cost
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if self.heating_energy_source == "Electricity":
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adjusted_heating_cost = adjusted_heating_kwh * AnnualBillSavings.ELECTRICITY_PRICE_CAP
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unadjusted_heating_cost = heating_prediction * AnnualBillSavings.ELECTRICITY_PRICE_CAP
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elif self.heating_energy_source == "Natural Gas":
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adjusted_heating_cost = adjusted_heating_kwh * AnnualBillSavings.GAS_PRICE_CAP
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unadjusted_heating_cost = heating_prediction * AnnualBillSavings.GAS_PRICE_CAP
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else:
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raise NotImplementedError("Not implemented cost for this fuel type")
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# Adjust today's cost figures with the UCL model
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adjusted_heating_cost = AnnualBillSavings.adjust_energy_cost_to_metered(
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epc_energy_cost=todays_heating_cost,
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current_epc_rating=self.data["current-energy-rating"],
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)
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if self.hot_water_energy_source == "Electricity":
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adjusted_hot_water_cost = adjusted_hot_water_kwh * AnnualBillSavings.ELECTRICITY_PRICE_CAP
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unadjusted_hot_water_cost = hot_water_prediction * AnnualBillSavings.ELECTRICITY_PRICE_CAP
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elif self.hot_water_energy_source == "Natural Gas":
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adjusted_hot_water_cost = adjusted_hot_water_kwh * AnnualBillSavings.GAS_PRICE_CAP
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unadjusted_hot_water_cost = hot_water_prediction * AnnualBillSavings.GAS_PRICE_CAP
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else:
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raise NotImplementedError("Not implemented cost for this fuel type")
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adjusted_hot_water_cost = AnnualBillSavings.adjust_energy_cost_to_metered(
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epc_energy_cost=todays_hot_water_cost,
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current_epc_rating=self.data["current-energy-rating"],
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)
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adjusted_lighting_cost = adjusted_lighting_kwh * AnnualBillSavings.ELECTRICITY_PRICE_CAP
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adjusted_lighting_cost = AnnualBillSavings.adjust_energy_cost_to_metered(
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epc_energy_cost=todays_lighting_cost,
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current_epc_rating=self.data["current-energy-rating"],
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)
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adjusted_appliances_cost = adjusted_applicances_kwh * AnnualBillSavings.ELECTRICITY_PRICE_CAP
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adjusted_appliances_cost = AnnualBillSavings.adjust_energy_cost_to_metered(
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epc_energy_cost=appliances_kwh * AnnualBillSavings.ELECTRICITY_PRICE_CAP,
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current_epc_rating=self.data["current-energy-rating"],
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)
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# Sum up the adjusted kwh figures
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self.current_adjusted_energy = (
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adjusted_heating_kwh + adjusted_hot_water_kwh + adjusted_lighting_kwh + adjusted_applicances_kwh
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)
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self.expected_energy_bill = (
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adjusted_heating_cost + adjusted_hot_water_cost + adjusted_lighting_cost + adjusted_appliances_cost
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)
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self.energy_cost_estimates = {
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"adjusted": {
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@ -730,9 +741,9 @@ class Property:
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"appliances": adjusted_appliances_cost
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},
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"unadjusted": {
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"heating": unadjusted_heating_cost,
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"hot_water": unadjusted_hot_water_cost,
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"lighting": float(self.data["lighting-cost-current"]),
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"heating": todays_heating_cost,
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"hot_water": todays_hot_water_cost,
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"lighting": todays_lighting_cost,
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"appliances": appliances_kwh * AnnualBillSavings.ELECTRICITY_PRICE_CAP
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},
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"epc": {
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@ -757,10 +768,6 @@ class Property:
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}
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}
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self.expected_energy_bill = (
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adjusted_heating_cost + adjusted_hot_water_cost + adjusted_lighting_cost + adjusted_appliances_cost
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)
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def set_spatial(self, spatial: pd.DataFrame):
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"""
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Sets whether the property is in a conservation area given the output of the ConservationAreaClient
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@ -359,7 +359,8 @@ async def trigger_plan(body: PlanTriggerRequest):
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},
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dummy_schema_path=f"model_directory/energy_consumption_model/{dataset_version}_dummy_schema.pkl",
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consumption_average_path=f"energy_consumption/{dataset_version}/consumption_averages.parquet",
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cleaned=cleaned
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cleaned=cleaned,
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environment=get_settings().ENVIRONMENT
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)
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logger.info("Getting spatial data")
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@ -1,11 +1,10 @@
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import pandas as pd
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import numpy as np
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from xgboost import XGBRegressor
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from datetime import datetime
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_percentage_error
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from sklearn.feature_selection import RFECV
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from utils.s3 import save_pickle_to_s3, read_pickle_from_s3, read_dataframe_from_s3_parquet
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from utils.s3 import save_pickle_to_s3, read_pickle_from_s3, read_dataframe_from_s3_parquet, read_csv_from_s3
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from utils.logger import setup_logger
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logger = setup_logger()
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@ -46,11 +45,17 @@ class EnergyConsumptionModel:
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"low-energy-lighting", "environment-impact-current", "energy-tariff", "current-energy-rating"
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]
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def __init__(self, cleaned, model_paths=None, dummy_schema_path=None, consumption_average_path=None, n_jobs=1):
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retail_price_comparison = None
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def __init__(
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self, cleaned, model_paths=None, dummy_schema_path=None, consumption_average_path=None, n_jobs=1,
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environment="dev"
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):
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self.cleaned = cleaned
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self.models = {}
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self.model_paths = model_paths or {}
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self.n_jobs = n_jobs
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self.environment = environment
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self.data = None
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self.input_data = None
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@ -76,26 +81,84 @@ class EnergyConsumptionModel:
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if model_paths:
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for target, path in model_paths.items():
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# Read model
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self.models[target] = read_pickle_from_s3(bucket_name="retrofit-model-directory-dev", s3_file_name=path)
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self.models[target] = read_pickle_from_s3(
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bucket_name=f"retrofit-model-directory-{environment}", s3_file_name=path
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)
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# Read dummy schema
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if dummy_schema_path:
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self.dummy_schema = read_pickle_from_s3(
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bucket_name="retrofit-model-directory-dev",
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bucket_name=f"retrofit-model-directory-{environment}",
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s3_file_name=dummy_schema_path
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)
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self.consumption_averages = None
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if consumption_average_path:
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self.consumption_averages = read_dataframe_from_s3_parquet(
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bucket_name="retrofit-data-dev",
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bucket_name=f"retrofit-data-{environment}",
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file_key=consumption_average_path
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)
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# We also retrieve the newest retail price comparison data which comes from Ofgem:
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# https://www.ofgem.gov.uk/energy-data-and-research/data-portal/retail-market-indicators
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# We use the detail price comparison by company and tariff type data
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self.read_retail_price_comparison()
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def read_retail_price_comparison(self):
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data = read_csv_from_s3(
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bucket_name=f"retrofit-data-{self.environment}",
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filepath="energy_consumption/retail-price-comparison.csv"
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)
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header = ['Date', 'Average standard variable tariff (Large legacy suppliers)',
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'Average standard variable tariff (Other suppliers)', 'Average fixed tariff',
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'Cheapest tariff (Large legacy suppliers)', 'Cheapest tariff (All suppliers)',
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'Cheapest tariff (Basket)', 'Default tariff cap level']
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# Extract data rows
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data_rows = []
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for row in data[1:]:
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date = row['\ufeff"']
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values = row[None]
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data_rows.append([date] + values)
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self.retail_price_comparison = pd.DataFrame(data_rows, columns=header)
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self.retail_price_comparison['Date'] = pd.to_datetime(self.retail_price_comparison['Date'], errors='coerce')
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def convert_cost_to_today(self, original_cost, lodgement_date):
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"""
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Given energy costs in an EPC, this function converts that energy cost to a figure based on today's energy costs
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(or as close to today as possible)
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:param original_cost: The original energy cost
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:param lodgement_date: The date the EPC was lodged
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:return:
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"""
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closest_date = self.retail_price_comparison.iloc[
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(self.retail_price_comparison['Date'] - lodgement_date).abs().argsort()[:1]
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]['Date'].values[0]
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closest_date = pd.Timestamp(closest_date)
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# Extract the tariff price on the closest date
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tariff_2024 = self.retail_price_comparison[
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self.retail_price_comparison['Date'] == closest_date
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]['Average standard variable tariff (Large legacy suppliers)'].values[0]
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# Extract the latest available tariff price
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latest_tariff = self.retail_price_comparison[
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'Average standard variable tariff (Large legacy suppliers)'
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].iloc[-1]
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# Calculate the ratio
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ratio = float(latest_tariff) / float(tariff_2024)
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# Calculate the updated heating cost
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updated_cost = original_cost * ratio
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return updated_cost
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def read_dataset(self, file_path):
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"""Reads the dataset from the specified file path."""
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logger.info(f"Reading dataset from {file_path}")
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self.data = read_dataframe_from_s3_parquet(bucket_name="retrofit-data-dev", file_key=file_path)
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self.data = read_dataframe_from_s3_parquet(bucket_name=f"retrofit-data-{self.environment}", file_key=file_path)
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self.input_data = self.data.copy()
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def feature_engineering(self, drop_first=False):
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@ -392,7 +455,7 @@ class EnergyConsumptionModel:
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logger.info(f"Saving model for target {target}")
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save_pickle_to_s3(
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self.models[target],
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bucket_name="retrofit-model-directory-dev",
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bucket_name=f"retrofit-model-directory-{self.environment}",
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s3_file_name=f"model_directory/energy_consumption_model/{target}_{dataset_version}.pkl"
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)
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@ -400,7 +463,7 @@ class EnergyConsumptionModel:
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logger.info("Saving dummy schema for target {target}")
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save_pickle_to_s3(
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self.dummy_schema,
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bucket_name="retrofit-model-directory-dev",
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bucket_name=f"retrofit-model-directory-{self.environment}",
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s3_file_name=f"model_directory/energy_consumption_model/{dataset_version}_dummy_schema.pkl"
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)
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