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added new pricing for air source heat pump
This commit is contained in:
Khalim Conn-Kowlessar
parent
b881c8358e
commit
aa515a9797
2 changed files with 79 additions and 81 deletions
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@ -1,6 +1,7 @@
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import numpy as np
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from recommendations.county_to_region import county_to_region_map
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from utils.logger import setup_logger
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from backend.ml_models.AnnualBillSavings import AnnualBillSavings
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logger = setup_logger()
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@ -21,25 +22,6 @@ regional_labour_variations = [
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{"Region": "Northern Ireland", "Adjustment_Factor": 0.76}
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]
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# This data is based on the MCS database - taken the figures for June 2024
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MCS_SOLAR_PV_COST_DATA = {
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"last_updated": "2024-07-10",
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"average_cost_per_kwh": 1825,
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"average_cost_per_kwh-Outer London": 1950,
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"average_cost_per_kwh-Inner London": 1950,
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"average_cost_per_kwh-South East England": 1966,
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"average_cost_per_kwh-South West England": 1864,
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"average_cost_per_kwh-East of England": 1719,
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"average_cost_per_kwh-East Midlands": 1730,
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"average_cost_per_kwh-West Midlands": 1789,
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"average_cost_per_kwh-North East England": 1872,
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"average_cost_per_kwh-North West England": 1860,
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"average_cost_per_kwh-Yorkshire and the Humber": 1789,
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"average_cost_per_kwh-Wales": 1676,
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"average_cost_per_kwh-Scotland": 1781,
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"average_cost_per_kwh-Northern Ireland": 1347,
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}
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# Installers are now working with 435 watt panels
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PANEL_SIZE = 0.435
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@ -61,47 +43,40 @@ INSTALLER_SOLAR_COSTS = [
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{'n_panels': 18, 'array_kwp': 18 * PANEL_SIZE, 'cost': 6792.57, 'installer': 'CEG'}
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]
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# These are costs we received from CRG, for pricing up air source heat pumps
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# These are costs that we have been provided from CRG specifically for air source heat pumps
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ASHP_SMALL_SYSTEM_COST = 8812.92 # 4.8 to 8.5, based on their pricing
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ASHP_LARGE_SYSTEM_COST = 11053.25
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ASHP_SECURITY = 455.00
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ASHP_WALL_BRACKET = 574.17
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ASHP_DISTRIBUTION_SYSTEM_COSTS = [
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{"n_radiators": 4, "cost": 3380.00},
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{"n_radiators": 5, "cost": 3607.50},
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{"n_radiators": 6, "cost": 4116.67},
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{"n_radiators": 7, "cost": 4647.50},
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{"n_radiators": 8, "cost": 5200.00},
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{"n_radiators": 9, "cost": 5730.83},
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{"n_radiators": 10, "cost": 6283.33},
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{"n_radiators": 11, "cost": 6857.50},
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{"n_radiators": 12, "cost": 7431.67},
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{"n_radiators": 13, "cost": 8016.67},
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{"n_radiators": 14, "cost": 8612.50},
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{"n_radiators": 15, "cost": 9219.17},
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{"n_radiators": 16, "cost": 9804.17},
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{"n_radiators": 17, "cost": 10389.17},
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]
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ASHP_CYLINDER_COSTS = [
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{"capacity_l": 120, "cost": 3318.25},
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{"capacity_l": 180, "cost": 3480.75},
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{"capacity_l": 200, "cost": 3853.42},
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{"capacity_l": 250, "cost": 3961.75},
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]
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# CEG uses use Solshare as an inverter to provide solar PV to multiple flats. This costs £7500 for the inverter alone
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# https://midsummerwholesale.co.uk/buy/solshare
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INSTALLER_SOLAR_PV_INVERTER_COST = 7500
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INSTALLER_SOLAR_PV_INVERTER_LABOUR_COST = 500 # Just a rough guess to labour costs
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# INSTALLER_SCAFFOLDING_COSTS = [
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# {'stories': 1, 'description': '1 Story Scaffold', 'cost': 531.00, 'installer': 'CEG'},
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# {'stories': 2, 'description': '2 Story Scaffold', 'cost': 841.00, 'installer': 'CEG'},
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# {'stories': 3, 'description': '3 Story Scaffold', 'cost': 1077.00, 'installer': 'CEG'}
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# ]
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# This data is based on the MCS database, We use the larger figure between the 2023 and 2024 average,
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# to be conservative
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MCS_AIR_SOURCE_HEAT_PUMP_COST_DATA = {
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"Outer London": 13220,
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"Inner London": 13220,
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"South East England": 13547,
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"South West England": 12776,
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"East of England": 12585,
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"East Midlands": 12239,
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"West Midlands": 13182,
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"North East England": 11829,
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"North West England": 11714,
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"Yorkshire and the Humber": 11919,
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"Wales": 13701,
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"Scotland": 12586,
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"Northern Ireland": 12000, # There are hardly any air source heat pump installs going on in Northern Ireland
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}
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INSTALLER_ASHP_COSTS = [
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{'capacity_kw': 5.0, 'brand': 'Mitsubishi', 'tank_size_liters': 150, 'cost': 10149.53, 'installer': 'CEG'},
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{'capacity_kw': 6.0, 'brand': 'Mitsubishi', 'tank_size_liters': 170, 'cost': 10823.48, 'installer': 'CEG'},
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{'capacity_kw': 8.5, 'brand': 'Mitsubishi', 'tank_size_liters': 200, 'cost': 11312.43, 'installer': 'CEG'},
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{'capacity_kw': 11.2, 'brand': 'Mitsubishi', 'tank_size_liters': 250, 'cost': 12156.75, 'installer': 'CEG'},
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{'capacity_kw': 14.0, 'brand': 'Mitsubishi', 'tank_size_liters': 300, 'cost': 14405.54, 'installer': 'CEG'},
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{'capacity_kw': 14.0, 'brand': 'Mitsubishi', 'tank_size_liters': 300, 'cost': 14405.54, 'installer': 'CEG'},
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{'capacity_kw': 17.0, 'brand': 'Grant', 'tank_size_liters': 300, 'cost': 14445.00, 'installer': 'CEG'},
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{'capacity_kw': 20.0, 'brand': 'Ecoforest', 'tank_size_liters': 400, 'cost': 21189.41, 'installer': 'CEG'},
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{'capacity_kw': None, 'brand': '2 x cascaded ASHPs', 'tank_size_liters': 500, 'cost': 22950.00, 'installer': 'CEG'}
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]
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INSTALLER_SOLAR_BATTERY_COSTS = [
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{'capacity_kwh': 5, 'description': 'Battery Add on', 'cost': 3769.89, 'installer': 'JJC'},
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# {'capacity_kwh': 10, 'description': 'Battery Add on', 'cost': 4300.00, 'installer': 'CEG'},
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@ -368,7 +343,7 @@ class Costs:
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total_cost = total_cost + labour_cost
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total_cost = round(total_cost, 2)
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total_cost = round(total_cost)
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return {
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"total": total_cost,
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@ -853,32 +828,55 @@ class Costs:
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"labour_days": labour_days,
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}
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def air_source_heat_pump(self, ashp_size):
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"""
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Based on the region and type of property, this function will produce a cost estimation for an air source heat
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pump. This cost will include the boiler upgrade scheme grant
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"""
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# This is the average cost of a project, we'll add some additional contingency
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if ashp_size is None:
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cost = [x for x in INSTALLER_ASHP_COSTS if x["capacity_kw"] is None][0]["cost"]
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@staticmethod
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def _select_cylinder_capacity(occupants: float):
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if occupants <= 2:
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return 120
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elif occupants <= 3:
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return 180
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elif occupants <= 4:
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return 200
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else:
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cost = [x for x in INSTALLER_ASHP_COSTS if x][0]["cost"]
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return 250
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# The costs from installers exclude VAT
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vat = cost * self.VAT_RATE
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cost = cost + vat
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def air_source_heat_pump(self, ashp_size: float, number_heated_rooms: int, total_floor_area: float) -> dict:
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"""
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We produce a cost estimation for an air source heat pump, based on costs we have received from installers.
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# We assume 5 days installation
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labour_days = 5
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labour_hours = labour_days * 8
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"""
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system_cost = (
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(ASHP_SMALL_SYSTEM_COST if ashp_size <= 8.5 else ASHP_LARGE_SYSTEM_COST) + ASHP_SECURITY + ASHP_WALL_BRACKET
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)
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available_n_rads = [x["n_radiators"] for x in ASHP_DISTRIBUTION_SYSTEM_COSTS]
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if number_heated_rooms < min(available_n_rads):
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# We use the smallest value
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rads_to_use = min(available_n_rads)
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elif number_heated_rooms > max(available_n_rads):
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# We use the largest value
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rads_to_use = max(available_n_rads)
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else:
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rads_to_use = int(number_heated_rooms)
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distribution_system_cost = [
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x for x in ASHP_DISTRIBUTION_SYSTEM_COSTS if x["n_radiators"] == rads_to_use
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][0]["cost"]
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# Cylinder cost
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est_n_occupants = AnnualBillSavings.calculate_occupants(total_floor_area)
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cylinder_capacity = self._select_cylinder_capacity(est_n_occupants)
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cylinder_cost = [
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x for x in ASHP_CYLINDER_COSTS if x["capacity_l"] == cylinder_capacity
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][0]["cost"]
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total = system_cost + distribution_system_cost + cylinder_cost
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return {
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"total": cost,
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"contingency": cost * self.CONTINGENCIES["air_source_heat_pump"],
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"total": total,
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"contingency": total * self.CONTINGENCIES["air_source_heat_pump"],
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"contingency_rate": self.CONTINGENCIES["air_source_heat_pump"],
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"vat": vat,
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"labour_hours": labour_hours,
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"labour_days": labour_days,
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"vat": 0,
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"labour_hours": 80,
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"labour_days": 10,
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}
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@ -526,14 +526,14 @@ class HeatingRecommender:
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# 1) Best available path: HLP → direct peak
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if heat_loss_parameter_W_per_m2K is not None:
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peak_kw = heat_loss_parameter_W_per_m2K * floor_area_m2 * ΔT / 1000.0
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return (peak_kw, peak_kw) # no range needed
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return peak_kw, peak_kw # no range needed
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# 2) Second-best: space-heating demand → HDD method
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if space_heat_kwh_per_m2_yr is not None:
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annual_space_kwh = space_heat_kwh_per_m2_yr * floor_area_m2
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Htot = annual_space_kwh * 1000.0 / (hdd_base_dd * 24.0) # W/K
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peak_kw = Htot * ΔT / 1000.0
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return (peak_kw, peak_kw)
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return peak_kw, peak_kw
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# 3) Minimal inputs: primary energy + assumed fraction → range
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assert epc_primary_kwh_per_m2_yr is not None
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@ -547,7 +547,7 @@ class HeatingRecommender:
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low = to_peak(space_heat_fraction_range[0])
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high = to_peak(space_heat_fraction_range[1])
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return (low, high)
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return low, high
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@staticmethod
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def pick_model(peak_kw_range, models_kw=(5, 6, 8.5, 11.2, 14, 17, 20)):
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