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handling more fuel types
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3 changed files with 7 additions and 2 deletions
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@ -82,6 +82,9 @@ DESCRIPTIONS_TO_FUEL_TYPES = {
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"Ground source heat pump, warm air, electric": {"fuel": "Electricity", "cop": AVERAGE_ASHP_EFFICIENCY / 100},
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"Room heaters, mains gas, Electric storage heaters": {"fuel": "Natural Gas", "cop": 0.85},
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"Water source heat pump, radiators, electric": {"fuel": "Electricity", "cop": AVERAGE_ASHP_EFFICIENCY / 100},
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"Air source heat pump, Systems with radiators, electric": {"fuel": "Electricity",
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"cop": AVERAGE_ASHP_EFFICIENCY / 100},
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"Ground source heat pump, underfloor, electric": {"fuel": "Electricity", "cop": AVERAGE_ASHP_EFFICIENCY / 100},
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}
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# These are the measure types where if there is a ventilation recommendation, we force the inclusion of it
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@ -286,7 +286,7 @@ class AnnualBillSavings:
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# The solar thermal covers a % of the heating kwh, so we need to adjust the cost
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return (kwh / cop) * assumptions.SOLAR_CONSUMPTION_PROPORTION * cls.GAS_PRICE_CAP
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if fuel == "Electricity + Solar Thermal":
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if fuel in ["Electricity + Solar Thermal", 'Unmapped + Solar Thermal']:
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# The solar thermal covers a % of the heating kwh, so we need to adjust the cost
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return (kwh / cop) * assumptions.SOLAR_CONSUMPTION_PROPORTION * cls.ELECTRICITY_PRICE_CAP
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@ -90,8 +90,10 @@ costs_by_floor_area = costs_by_floor_area.groupby("current-energy-efficiency")[
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["lighting-cost-current_scaled", "heating-cost-current_scaled", "hot-water-cost-current_scaled"]
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].mean().reset_index()
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epc_data = epc_data[~pd.isnull(epc_data["UPRN"])]
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sample_epc_data = epc_data[pd.to_datetime(epc_data["LODGEMENT_DATE"]) >= "2015-01-01"].drop_duplicates("UPRN").sample(
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3000).reset_index(drop=True)
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5000).reset_index(drop=True)
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# TODO: In Property find_energy_sources, sort out biomass community heating - what fuel type
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# TODO: We might be able to remove find_energy_sources entirely and remove estimate_electrical_consumption. It's used
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