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117 lines
4.1 KiB
Python
117 lines
4.1 KiB
Python
class AnnualBillSavings:
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"""
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This is a simple class which will estimate the annual bill savings, based on the kwh savings.
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This class uses data from Ofgem, including their price caps, to provide us with an estimate for
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1KWH of energy.
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"""
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# These gas an electricity consumption figures are based off of figures presented by Ofgem
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# https://www.ofgem.gov.uk/information-consumers/energy-advice-households/average-gas-and-electricity-use-explained
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AVERAGE_ELECTRICITY_CONSUMPTION = 2700
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AVERAGE_GAS_CONSUMPTION = 11500
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# Latest price cap figures from Ofgem are for April 2024
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# https://www.ofgem.gov.uk/publications/new-energy-price-cap-level-april-june-2024-starts-today
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ELECTRICITY_PRICE_CAP = 0.245
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GAS_PRICE_CAP = 0.0604
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# This is a weighted mean of the price caps, using the consumption figures above as weights
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PRICE_FACTOR = 0.09549999999999999
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# Daily standard charge, based on average across England, Scotland and Wales, and includes VAT
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DAILY_STANDARD_CHARGE_GAS = 0.3143
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DAILY_STANDARD_CHARGE_ELECTRICITY = 0.601
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EPC_BANDS = ["G", "F", "E", "D", "C", "B", "A"]
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@classmethod
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def estimate(cls, kwh: float):
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"""
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Estimate the annual bill savings based on the kwh savings
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:param kwh: The kwh savings
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:return: An estimate for annual bill savings
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"""
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return cls.PRICE_FACTOR * kwh
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@classmethod
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def estimate_electric(cls, kwh: float):
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"""
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Estimate the annual bill savings based on the kwh savings
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:param kwh: The kwh savings
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:return: An estimate for annual bill savings
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"""
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return cls.ELECTRICITY_PRICE_CAP * kwh
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@classmethod
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def calculate_annual_bill(cls, kwh):
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"""
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This method will estimate the total annual bill for a property
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It assumed gas & electricity are used
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:param kwh: The total kwh consumption
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:return: An estimate for annual bill
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"""
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return cls.PRICE_FACTOR * kwh + (cls.DAILY_STANDARD_CHARGE_GAS + cls.DAILY_STANDARD_CHARGE_ELECTRICITY * 365)
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@classmethod
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def adjust_energy_to_metered(cls, epc_energy_consumption, current_epc_rating):
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"""
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The over-prediction of energy use by EPCs in Great Britain: A comparison
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of EPC-modelled and metered primary energy use intensity
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Which can be found here: https://www.sciencedirect.com/science/article/pii/S0378778823002542
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We implement the results on page 10
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:return:
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"""
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gradients = {
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"A": -0.1,
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"B": -0.1,
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"C": -0.43,
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"D": -0.52,
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"E": -0.7,
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"F": -0.76,
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"G": -0.76
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}
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intercepts = {
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"A": 28,
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"B": 28,
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"C": 97,
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"D": 119,
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"E": 160,
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"F": 157,
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"G": 157
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}
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gradient = gradients[current_epc_rating]
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intercept = intercepts[current_epc_rating]
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# This should be negative
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consumption_difference = gradient * epc_energy_consumption + intercept
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adjusted_consumption = (epc_energy_consumption + consumption_difference)
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if adjusted_consumption < 0:
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raise ValueError("consumption_difference should be negative")
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return adjusted_consumption
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@classmethod
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def adjust_expected_band(cls, expected_epc_rating, current_epc_rating):
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"""
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Because of the differing intercepts and intercepts when adjusting, it's possible for
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expected_adjusted_energy to be bigger than current_adjusted_energy. In this case, we'll
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adjust, against at most 1 EPC band above the curent. This function performs the EPC adjustment
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:param expected_epc_rating: The expected EPC rating
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:param current_epc_rating: The current EPC rating
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"""
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# Find index of expected EPC rating
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expected_index = cls.EPC_BANDS.index(expected_epc_rating)
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current_index = cls.EPC_BANDS.index(current_epc_rating)
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if expected_index - 1 < current_index:
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return current_epc_rating
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return cls.EPC_BANDS[expected_index - 1]
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