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got the recommendation impact working for pfp
This commit is contained in:
Khalim Conn-Kowlessar
parent
a28c94c18b
commit
14450f2b79
3 changed files with 115 additions and 67 deletions
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@ -739,7 +739,7 @@ class Property:
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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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self.current_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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@ -501,7 +501,9 @@ async def trigger_plan(body: PlanTriggerRequest):
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Recommendations.calculate_recommendation_impact(
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property_instance=property_instance,
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all_predictions=all_predictions,
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recommendations=recommendations
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recommendations=recommendations,
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representative_recommendations=representative_recommendations,
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energy_consumption_client=energy_consumption_client
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)
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)
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@ -277,9 +277,71 @@ class Recommendations:
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return property_recommendations
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@staticmethod
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def _calculate_appliance_solar_savings(
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rec, property_instance, heating_kwh_reduction, hot_water_kwh_reduction, lighting_kwh_reduction
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):
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"""
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Calculates the impact on kwh and cost of installing solar panels on appliances
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:param rec: The recommendation
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:param property_instance: Instance of the Property class
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:param heating_kwh_reduction: The kwh reduction from heating
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:param hot_water_kwh_reduction: The kwh reduction from hot water
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:param lighting_kwh_reduction: The kwh reduction from lighting
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:return:
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"""
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if rec["type"] != "solar_pv":
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return 0, 0
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# Calulate the amount of energy the solar panel array will generate for this unit
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unit_energy_consumption = (
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rec["initial_ac_kwh_per_year"] *
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property_instance.solar_panel_configuration["unit_share_of_energy"]
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)
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unit_energy_utilised = unit_energy_consumption * GoogleSolarApi.SOLAR_CONSUMPTION_PROPORTION
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unit_energy_exported = unit_energy_consumption - unit_energy_utilised
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unit_energy_exported_value = unit_energy_exported * AnnualBillSavings.ELECTRICITY_EXPORT_PAYMENT
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# We assume that 50% of the energy generated will be used by the property without a battery
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# to be conservative
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# of the energy utilised, some of it is used by heating, hot water and lighting so we
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# remove that from the total
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unit_energy_utilised -= (
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heating_kwh_reduction + hot_water_kwh_reduction + lighting_kwh_reduction
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)
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unit_energy_utilised = 0 if unit_energy_utilised < 0 else unit_energy_utilised
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# This is how much energy the appliances will use after install
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post_install_appliance_kwh = (
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property_instance.energy_consumption_estimates["adjusted"]["appliances"] -
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unit_energy_utilised
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)
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post_install_appliance_kwh = (
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0 if post_install_appliance_kwh < 0 else post_install_appliance_kwh
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)
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predicted_appliances_kwh_reduction = (
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property_instance.energy_consumption_estimates["adjusted"]["appliances"] -
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post_install_appliance_kwh
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)
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predicted_appliances_cost_reduction = unit_energy_exported_value + (
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predicted_appliances_kwh_reduction * AnnualBillSavings.ELECTRICITY_PRICE_CAP
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)
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return predicted_appliances_cost_reduction, predicted_appliances_kwh_reduction
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@classmethod
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def calculate_recommendation_impact(
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cls, property_instance, all_predictions, recommendations, energy_consumption_client
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cls,
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property_instance,
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all_predictions,
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recommendations,
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representative_recommendations,
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energy_consumption_client
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):
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"""
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@ -289,6 +351,7 @@ class Recommendations:
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:param property_instance: Instance of the Property class, for the home associated to property_id
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:param all_predictions: dictionary of predictions from the model apis
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:param recommendations: dictionary of recommendations for the property
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:param representative_recommendations: dictionary of representative recommendations for the property
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:param energy_consumption_client: Instance of the EnergyConsumptionClient class
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:return:
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"""
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@ -350,8 +413,14 @@ class Recommendations:
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].median().reset_index()
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)
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representative_rec_ids = [
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rec["recommendation_id"] for rec in representative_recommendations[property_instance.id]
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]
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phase_lighting_costs = {}
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phase_kwh_figures = {}
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bill_savings_list = []
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kwh_savings_list = []
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for recommendations_by_type in property_recommendations:
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for rec in recommendations_by_type:
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@ -447,21 +516,6 @@ class Recommendations:
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"unadjusted": scoring_lighting_cost
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}
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# This is the total bill savings for the recommendation
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if rec["type"] == "solar_pv":
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# We need to calculate the predicted bill savings for the solar pv recommendation
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# where we will get some savings from the cost of appliances but it depends on the amount
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# of energy generated by the solar panels
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# We can assume that 50% of the energy generated will be used by the property without
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# a battery, to be conservative.
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# SIMILARLY: We need to handle kwh savings
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raise Exception("Handle me")
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else:
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predicted_bill_savings = (
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predicted_heating_cost_reduction + predicted_hot_water_cost_reduction +
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predicted_lighting_cost_reduction
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)
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# We now predict the kwh savings using the xgb model
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simulation_epc = property_instance.simulation_epcs[rec["phase"]].copy()
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@ -507,7 +561,30 @@ class Recommendations:
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lighting_kwh_reduction = predicted_lighting_cost_reduction / AnnualBillSavings.ELECTRICITY_PRICE_CAP
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kwh_reduction = heating_kwh_reduction + hot_water_kwh_reduction + lighting_kwh_reduction
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(
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predicted_appliances_cost_reduction,
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predicted_appliances_kwh_reduction
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) = cls._calculate_appliance_solar_savings(
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rec=rec,
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property_instance=property_instance,
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heating_kwh_reduction=heating_kwh_reduction,
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hot_water_kwh_reduction=hot_water_kwh_reduction,
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lighting_kwh_reduction=lighting_kwh_reduction
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)
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kwh_reduction = (
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heating_kwh_reduction +
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hot_water_kwh_reduction +
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lighting_kwh_reduction +
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predicted_appliances_kwh_reduction
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)
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predicted_bill_savings = (
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predicted_heating_cost_reduction +
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predicted_hot_water_cost_reduction +
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predicted_lighting_cost_reduction +
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predicted_appliances_cost_reduction
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)
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phase_kwh_figures[rec["phase"]] = {
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"adjusted": {
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@ -651,48 +728,16 @@ class Recommendations:
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lighting_kwh_reduction = predicted_lighting_cost_reduction / AnnualBillSavings.ELECTRICITY_PRICE_CAP
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# This is the total bill savings for the recommendation
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predicted_appliances_cost_reduction = 0
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predicted_appliances_kwh_reduction = 0
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if rec["type"] == "solar_pv":
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# Calulate the amount of energy the solar panel array will generate for this unit
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unit_energy_consumption = (
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rec["initial_ac_kwh_per_year"] *
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property_instance.solar_panel_configuration["unit_share_of_energy"]
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)
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unit_energy_utilised = unit_energy_consumption * GoogleSolarApi.SOLAR_CONSUMPTION_PROPORTION
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unit_energy_exported = unit_energy_consumption - unit_energy_utilised
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unit_energy_exported_value = unit_energy_exported * AnnualBillSavings.ELECTRICITY_EXPORT_PAYMENT
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# We assume that 50% of the energy generated will be used by the property without a battery
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# to be conservative
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# of the energy utilised, some of it is used by heating, hot water and lighting so we
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# remove that from the total
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unit_energy_utilised -= (
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heating_kwh_reduction + hot_water_kwh_reduction + lighting_kwh_reduction
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)
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unit_energy_utilised = 0 if unit_energy_utilised < 0 else unit_energy_utilised
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# This is how much energy the appliances will use after install
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post_install_appliance_kwh = (
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property_instance.energy_consumption_estimates["adjusted"]["appliances"] -
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unit_energy_utilised
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)
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post_install_appliance_kwh = (
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0 if post_install_appliance_kwh < 0 else post_install_appliance_kwh
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)
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predicted_appliances_kwh_reduction = (
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property_instance.energy_consumption_estimates["adjusted"]["appliances"] -
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post_install_appliance_kwh
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)
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predicted_appliances_cost_reduction = unit_energy_exported_value + (
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predicted_appliances_kwh_reduction * AnnualBillSavings.ELECTRICITY_PRICE_CAP
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)
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(
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predicted_appliances_cost_reduction,
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predicted_appliances_kwh_reduction
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) = cls._calculate_appliance_solar_savings(
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rec=rec,
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property_instance=property_instance,
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heating_kwh_reduction=heating_kwh_reduction,
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hot_water_kwh_reduction=hot_water_kwh_reduction,
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lighting_kwh_reduction=lighting_kwh_reduction
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)
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# We now calculate the predicted_bill_savings
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predicted_bill_savings = (
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@ -723,17 +768,18 @@ class Recommendations:
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rec["kwh_savings"] = kwh_reduction
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rec["energy_cost_savings"] = predicted_bill_savings
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if rec["recommendation_id"] in representative_rec_ids:
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bill_savings_list.append(predicted_bill_savings)
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kwh_savings_list.append(kwh_reduction)
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if (rec["sap_points"] is None) and (rec["co2_equivalent_savings"] is None) or (
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rec["heat_demand"] is None) or (rec["energy_cost_savings"] is None):
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raise ValueError("sap points, co2 or heat demand is missing")
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# We sum up the total savings for the property and that is our expected energy bill
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# expected_energy_bill = sum(
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# [
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# rec["energy_cost_savings"] for rec in property_recommendations
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# if rec["type"] != "mechanical_ventilation"
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# ]
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# )
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expected_energy_bill = property_instance.current_energy_bill - sum(bill_savings_list)
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expected_adjusted_energy = property_instance.current_adjusted_energy - sum(kwh_savings_list)
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return (
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property_recommendations,
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