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78 lines
4.1 KiB
Python
78 lines
4.1 KiB
Python
from recommendations.Costs import Costs
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from recommendations.recommendation_utils import check_simulation_difference
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from backend.Property import Property
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from etl.epc_clean.epc_attributes.MainheatControlAttributes import MainheatControlAttributes
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class HeatingControlRecommender:
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def __init__(self, property_instance: Property):
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self.property = property_instance
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self.costs = Costs(self.property)
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self.recommendations = []
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def recommend(self, phase=0):
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# This first iteration of the recommender will provide very basic recommendation
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# We recommend heating controls based on the main heating system
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if self.property.main_heating["clean_description"] == "Room heaters, electric":
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self.recommend_room_heaters_electric_controls(phase=phase)
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return
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def recommend_room_heaters_electric_controls(self, phase):
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"""
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If the home has Room heaters, electric, we start by identifying potential heating controls that could
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be upgraded, that would provide a practical impact. This will be the least invasive improvement.
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We can then consider the heating system itself
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:return:
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"""
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if (self.property.data["mainheatc-energy-eff"] in ["Poor", "Very Poor", "Average"]) or (
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self.property.main_heating_controls["clean_description"] in ["Programmer and room thermostat"]
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):
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# We recommend Programmer and appliance thermostats as the heating control. This has an average energy
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# efficiency rating, and is likely to be more efficient than the current heating controls. if the
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# rating is poor or very poor, the home may have a Programmer and room thermostat, which is less efficient
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# than a Programmer and appliance thermostats, because it allows for much more granular control at not
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# just a room level but individual heater/appliance level
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# Note: A room thermostat is commonly placed in a hallway, and it measures the temperature of the air
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# surrounding it. It then sends a signal to the heating system to turn on or off, depending on the
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# temperature. An appliance thermostat, on the other hand, is placed on the heater/appliance itself, and
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# measures the temperature of the heater/appliance. This allows for much more granular control, and
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# prevents overheating.
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# In order to cost, we check if the property already has a programmer, and therefor we will just need to
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# add the cost of the appliance thermostats
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has_programmer = self.property.main_heating_controls["switch_system"] == "programmer"
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ending_config = MainheatControlAttributes("Programmer and appliance thermostats").process()
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# We look at what has changed in the ending config, and compare it to the current config
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# We use this to determine how we should be updating the config
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simulation_config = check_simulation_difference(
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new_config=ending_config, old_config=self.property.main_heating_controls
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)
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# This upgrade will only take the heating system to average energy efficiency
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simulation_config["mainheatc_energy_eff_ending"] = "Good"
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self.recommendations.append(
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{
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"phase": phase,
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"parts": [
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# TODO
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],
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"type": "heating_control",
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"description": "Upgrade heating controls to Programmer and Appliance or Smart "
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"Thermostats for more precise heating control, and prevention of overheating",
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"starting_u_value": None,
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"new_u_value": None,
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"sap_points": None,
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**self.costs.programmer_and_appliance_thermostat(has_programmer=has_programmer),
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"simulation_config": simulation_config
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}
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)
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# We don't implement any other recommendations right now
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return
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