mirror of
https://github.com/Hestia-Homes/Model.git
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Added in second part of recommendation impact calculations
This commit is contained in:
Khalim Conn-Kowlessar
parent
af66744979
commit
3bdc4db92a
4 changed files with 226 additions and 35 deletions
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@ -380,6 +380,7 @@ async def trigger_plan(body: PlanTriggerRequest):
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target_rating=body.goal_value,
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current_consumption=p.current_adjusted_energy
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),
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"property_id": p.id
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} for p in input_properties if p.building_id is not None
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]
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if building_ids:
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@ -419,7 +420,13 @@ async def trigger_plan(body: PlanTriggerRequest):
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# Insert this into the properties that have this building id
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for p in input_properties:
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if p.building_id == building_id:
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p.set_solar_panel_configuration(solar_panel_configuration[building_id])
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unit_solar_panel_configuration = solar_panel_configuration[building_id].copy()
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unit_solar_panel_configuration["unit_share_of_energy"] = (
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[x for x in building_ids if x["property_id"] == p.id][0]["energy_consumption"] /
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energy_consumption
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)
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p.set_solar_panel_configuration(unit_solar_panel_configuration)
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else:
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# Model the solar potential at the property level
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@ -28,8 +28,10 @@ class AnnualBillSavings:
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# https://www.ofgem.gov.uk/energy-price-cap
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ELECTRICITY_PRICE_CAP = 0.2236
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GAS_PRICE_CAP = 0.0548
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# This is the most recent export payment figure, at 12p per kwh
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ELECTRICITY_EXPORT_PAYMENT = 0.12
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# This is the most recent export payment figure, at 9.28p/kWh
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# Smart export guarantee rates can be found here:
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# https://www.sunsave.energy/solar-panels-advice/exporting-to-the-grid/best-seg-rates
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ELECTRICITY_EXPORT_PAYMENT = 0.0928
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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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@ -14,6 +14,7 @@ from recommendations.HeatingRecommender import HeatingRecommender
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from recommendations.HotwaterRecommendations import HotwaterRecommendations
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from recommendations.SecondaryHeating import SecondaryHeating
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from backend.ml_models.AnnualBillSavings import AnnualBillSavings
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from backend.apis.GoogleSolarApi import GoogleSolarApi
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class Recommendations:
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@ -374,6 +375,8 @@ class Recommendations:
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# # TODO: We should determine if the home is gas & electricity or just electricity
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# expected_energy_bill = AnnualBillSavings.calculate_annual_bill(expected_adjusted_energy)
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phase_lighting_costs = {}
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phase_kwh_figures = {}
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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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@ -422,23 +425,52 @@ class Recommendations:
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float(property_instance.data["energy-consumption-current"]) - new_heat_demand
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)
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if rec["type"] == "lighting":
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new_heating_cost = property_instance.energy_cost_estimates["adjusted"]["heating"]
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new_hot_water_cost = property_instance.energy_cost_estimates["adjusted"]["hot_water"]
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new_lighting_cost = min(
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new_lighting_cost, property_instance.energy_cost_estimates["adjusted"]["lighting"]
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)
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scoring_heating_cost = property_instance.energy_cost_estimates["unadjusted"]["heating"]
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scoring_hot_water_cost = property_instance.energy_cost_estimates["unadjusted"]["hot_water"]
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scoring_lighting_cost = min(
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property_instance.energy_cost_estimates["unadjusted"]["lighting"],
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new_lighting_cost_unadjusted
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)
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else:
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new_heating_cost = min(
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new_heating_cost, property_instance.energy_cost_estimates["adjusted"]["heating"]
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)
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new_hot_water_cost = min(
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new_hot_water_cost, property_instance.energy_cost_estimates["adjusted"]["hot_water"]
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)
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new_lighting_cost = property_instance.energy_cost_estimates["adjusted"]["lighting"]
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scoring_heating_cost = min(
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property_instance.energy_cost_estimates["unadjusted"]["heating"],
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new_heating_cost_unadjusted
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)
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scoring_hot_water_cost = min(
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property_instance.energy_cost_estimates["unadjusted"]["hot_water"],
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new_hot_water_cost_unadjusted
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)
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scoring_lighting_cost = property_instance.energy_cost_estimates["unadjusted"]["lighting"]
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predicted_heating_cost_reduction = (
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float(property_instance.energy_cost_estimates["adjusted"]["heating"]) - new_heating_cost
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property_instance.energy_cost_estimates["adjusted"]["heating"] - new_heating_cost
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)
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predicted_hot_water_cost_reduction = (
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float(property_instance.energy_cost_estimates["adjusted"]["hot_water"]) - new_hot_water_cost
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)
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predicted_heating_cost_reduction = (
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0 if predicted_heating_cost_reduction < 0 else predicted_heating_cost_reduction
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)
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predicted_hot_water_cost_reduction = (
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0 if predicted_hot_water_cost_reduction < 0 else predicted_hot_water_cost_reduction
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property_instance.energy_cost_estimates["adjusted"]["hot_water"] - new_hot_water_cost
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)
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# Only lighting recommendations can have an impact here
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predicted_lighting_cost_reduction = 0 if rec["type"] != "lighting" else (
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float(property_instance.energy_cost_estimates["adjusted"]["lighting"]) - new_lighting_cost
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property_instance.energy_cost_estimates["adjusted"]["lighting"] - new_lighting_cost
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)
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# We store this value for later
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phase_lighting_costs[rec["phase"]] = {
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"adjusted": new_lighting_cost,
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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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@ -456,17 +488,6 @@ class Recommendations:
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)
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# We now predict the kwh savings using the xgb model
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scoring_heating_cost = min(
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property_instance.energy_cost_estimates["unadjusted"]["heating"], new_heating_cost_unadjusted
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)
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scoring_hot_water_cost = min(
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property_instance.energy_cost_estimates["unadjusted"]["hot_water"],
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new_hot_water_cost_unadjusted
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)
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scoring_lighting_cost = min(
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property_instance.energy_cost_estimates["unadjusted"]["lighting"], new_lighting_cost_unadjusted
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) if rec["type"] == "lighting" \
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else property_instance.energy_cost_estimates["unadjusted"]["lighting"]
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simulation_epc = property_instance.simulation_epcs[rec["phase"]].copy()
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# The current heating, hot water and energy kwh should be based on the new, unadjusted
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@ -513,6 +534,17 @@ class Recommendations:
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kwh_reduction = heating_kwh_reduction + hot_water_kwh_reduction + lighting_kwh_reduction
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phase_kwh_figures[rec["phase"]] = {
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"adjusted": {
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"heating": new_heating_kwh_adjusted,
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"hot_water": new_hot_water_kwh_adjusted
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},
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"unadjusted": {
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"heating": new_heating_kwh,
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"hot_water": new_hot_water_kwh
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}
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}
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else:
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previous_phase = rec["phase"] - 1
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predicted_sap_points = (
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@ -527,30 +559,177 @@ class Recommendations:
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new_heat_demand
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)
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if rec["type"] == "lighting":
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# If we have a lighting recommendation, the heating, hot water and lighting costs will
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# be from the previous phase - nothing will change
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new_heating_cost = heating_cost_phase_impact[
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heating_cost_phase_impact["phase"] == previous_phase
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]["adjusted_cost"].values[0]
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new_hot_water_cost = hot_water_cost_phase_impact[
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hot_water_cost_phase_impact["phase"] == previous_phase
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]["adjusted_cost"].values[0]
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new_lighting_cost = min(
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new_lighting_cost, phase_lighting_costs[previous_phase]["adjusted"]
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)
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# We also use the unadjusted costs for the scoring from the previous phase
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scoring_heating_cost = heating_cost_phase_impact[
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heating_cost_phase_impact["phase"] == previous_phase
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]["predictions"].values[0]
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scoring_hot_water_cost = hot_water_cost_phase_impact[
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hot_water_cost_phase_impact["phase"] == previous_phase
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]["predictions"].values[0]
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scoring_lighting_cost = min(
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new_lighting_cost_unadjusted,
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phase_lighting_costs[previous_phase]["unadjusted"]
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)
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else:
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# Whereas for other recommendations, we use the new costs
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new_heating_cost = min(
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new_heating_cost,
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heating_cost_phase_impact[
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heating_cost_phase_impact["phase"] == previous_phase
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]["adjusted_cost"].values[0]
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)
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new_hot_water_cost = min(
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new_hot_water_cost,
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hot_water_cost_phase_impact[
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hot_water_cost_phase_impact["phase"] == previous_phase
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]["adjusted_cost"].values[0]
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)
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new_lighting_cost = phase_lighting_costs[previous_phase]["adjusted"]
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scoring_heating_cost = min(
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new_heating_cost_unadjusted,
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heating_cost_phase_impact[
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heating_cost_phase_impact["phase"] == previous_phase
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]["predictions"].values[0]
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)
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scoring_hot_water_cost = min(
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new_hot_water_cost_unadjusted,
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hot_water_cost_phase_impact[
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hot_water_cost_phase_impact["phase"] == previous_phase
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]["predictions"].values[0]
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)
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scoring_lighting_cost = phase_lighting_costs[previous_phase]["unadjusted"]
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# We now estimate the adjusted cost savings for the recommendation
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predicted_heating_cost_reduction = (
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heating_cost_phase_impact[heating_cost_phase_impact["phase"] == previous_phase][
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"adjusted_cost"
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].values[0] - new_heating_cost
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)
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predicted_heating_cost_reduction = (
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0 if predicted_heating_cost_reduction < 0 else predicted_heating_cost_reduction
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)
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predicted_hot_water_cost_reduction = (
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hot_water_cost_phase_impact[hot_water_cost_phase_impact["phase"] == previous_phase][
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"adjusted_cost"
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].values[0] - new_hot_water_cost
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)
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predicted_hot_water_cost_reduction = (
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0 if predicted_hot_water_cost_reduction < 0 else predicted_hot_water_cost_reduction
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)
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# Only lighting recommendations can have an impact here
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predicted_lighting_cost_reduction = 0 if rec["type"] != "lighting" else (
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lighting_cost_phase_impact[lighting_cost_phase_impact["phase"] == previous_phase][
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"adjusted_cost"
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].values[0] - new_lighting_cost
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predicted_lighting_cost_reduction = (
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phase_lighting_costs[previous_phase]["adjusted"] - new_lighting_cost
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)
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# We now predict the kwh savings using the xgb model - this is based on
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# the new costs at this phase
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simulation_epc = property_instance.simulation_epcs[rec["phase"]].copy()
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# The current heating, hot water and energy kwh should be based on the new, unadjusted
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# costs for lighting, heating, hot water
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simulation_epc["heating-cost-current"] = int(scoring_heating_cost)
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simulation_epc["hot-water-cost-current"] = int(scoring_hot_water_cost)
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simulation_epc["lighting-cost-current"] = int(scoring_lighting_cost)
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# We predict with the energy consumption model
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scoring_df = pd.DataFrame([simulation_epc])
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# Change columns from underscores to hyphens
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scoring_df.columns = [
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x.lower().replace("_", "-") for x in scoring_df.columns
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]
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for col in ["heating_kwh", "hot_water_kwh"]:
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scoring_df[col] = None
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energy_consumption_client.data = None
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new_heating_kwh = energy_consumption_client.score_new_data(
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new_data=scoring_df, target="heating_kwh"
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)[0]
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new_hot_water_kwh = energy_consumption_client.score_new_data(
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new_data=scoring_df, target="hot_water_kwh"
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)[0]
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# Adjust these figures
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new_heating_kwh_adjusted = AnnualBillSavings.adjust_energy_to_metered(
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new_heating_kwh, current_epc_rating=property_instance.data["current-energy-rating"]
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)
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new_hot_water_kwh_adjusted = AnnualBillSavings.adjust_energy_to_metered(
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new_hot_water_kwh, current_epc_rating=property_instance.data["current-energy-rating"]
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)
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heating_kwh_reduction = 0 if predicted_heating_cost_reduction == 0 else (
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phase_kwh_figures[previous_phase]["adjusted"]["heating"] - new_heating_kwh_adjusted
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)
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hot_water_kwh_reduction = 0 if predicted_hot_water_cost_reduction == 0 else (
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phase_kwh_figures[previous_phase]["adjusted"]["hot_water"] - new_hot_water_kwh_adjusted
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)
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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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# We now calculate the predicted_bill_savings
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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 + predicted_appliances_cost_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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if rec["type"] == "low_energy_lighting":
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@ -113,13 +113,15 @@ class SolarPvRecommendations:
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for rank, recommendation_config in best_configurations.iterrows():
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roof_coverage_percent = round(recommendation_config["panneled_roof_area"] / total_roof_area * 100)
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# Spread the cost to the individual units
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# Spread the cost to the individual units - adding a 20% contingency
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total_cost = recommendation_config["total_cost"] / n_units
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kw = np.floor(recommendation_config["array_warrage"] / 100) / 10
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description = (f"Install a {kw} kilowatt-peak (kWp) solar photovoltaic (PV) panel system on the roof "
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"of the building")
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initial_ac_kwh_per_year = recommendation_config["initial_ac_kwh_per_year"]
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self.recommendation.append(
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{
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"phase": phase,
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@ -135,6 +137,7 @@ class SolarPvRecommendations:
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# back up here
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"photo_supply": roof_coverage_percent,
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"has_battery": False,
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"initial_ac_kwh_per_year": initial_ac_kwh_per_year,
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"description_simulation": {"photo-supply": roof_coverage_percent},
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"rank": rank # Rank is used to get the representative recommendation - rank 0 will be chosen
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}
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