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updating recommender update

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Khalim Conn-Kowlessar 2024-08-07 17:56:07 +01:00
parent 48f21e6edf
commit 25c07fdc52
3 changed files with 33 additions and 28 deletions
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3
backend/Property.py
View file

@ -475,6 +475,9 @@ class Property:
"lighting-cost-current": phase_impact["unadjusted_lighting_cost"],
}
)
updated_simulation_epcs.append(sim_epc)
return updated_simulation_epcs
@staticmethod
def create_recommendation_scoring_data(

4
backend/app/plan/router.py
View file

@ -802,6 +802,7 @@ async def trigger_plan(body: PlanTriggerRequest):
# cylinder jacket), we should add these to the recommendations as default
raise Exception("Add the cost impacts into the cost model")
logger.info("Optimising recommendations")
scoring_epcs = [] # For scoring the kwh models
for property_id in recommendations.keys():
property_instance = [p for p in input_properties if p.id == property_id][0]
@ -816,7 +817,8 @@ async def trigger_plan(body: PlanTriggerRequest):
# We use the impact_summary to update the simulation_epcs with the new SAP, heat demand, carbon, cost etc
# at each phase
property_instance.update_simulation_epcs(impact_summary)
property_scoring_epcs = property_instance.update_simulation_epcs(impact_summary)
scoring_epcs.extend(property_scoring_epcs)
input_measures = prepare_input_measures(recommendations_with_impact, body.goal)

54
recommendations/Recommendations.py
View file

@ -432,32 +432,19 @@ class Recommendations:
# We structure this so that depending on the phase, we capture the previous phase impacts and
# then just have one piece of code to calculate the difference
if rec["phase"] == 0:
# These are just the starting values, from the EPC. When we score the ML models,
# heating_cost_starting and heating_cost_ending are just the values in the EPC. However, with
# heating_cost_ending, we expect that the EPC will predict a heating cost based on what would happen
# if we implemented the recommendation today, so our starting value is the EPC
previous_phase_values = {
"sap": float(property_instance.data["current-energy-efficiency"]),
"carbon": float(property_instance.data["co2-emissions-current"]),
"heat_demand": float(property_instance.data["energy-consumption-current"]),
"epc_heating_cost": float(property_instance.data["heating-cost-current"]),
"epc_hot_water_cost": float(property_instance.data["hot-water-cost-current"]),
"epc_lighting_cost": float(property_instance.data["lighting-cost-current"])
}
if rec["type"] == "low_energy_lighting":
# In this instance, heating cost and hot water cost should not change so we set the previous
# value to the new one, so the difference is zero
previous_phase_unadjusted_costs = {
"unadjusted_heating_cost": phase_cost["heating_cost"]["predictions"].values[0],
"unadjusted_hot_water_cost": phase_cost["hot_water_cost"]["predictions"].values[0],
"unadjusted_lighting_cost": (
property_instance.energy_cost_estimates["unadjusted"]["lighting"]
)
}
else:
# If the recommendaiton is not for low energy lighting, we expect the heating/hot water
# costs to change but not te lighting
previous_phase_unadjusted_costs = {
"unadjusted_heating_cost": property_instance.energy_cost_estimates["adjusted"]["heating"],
"unadjusted_hot_water_cost": (
property_instance.energy_cost_estimates["adjusted"]["hot_water"]
),
"unadjusted_lighting_cost": phase_cost["lighting_cost"]["predictions"].values[0]
}
else:
previous_phase_values = {
"sap": (
@ -497,8 +484,6 @@ class Recommendations:
"unadjusted_lighting_cost": phase_cost["lighting_cost"]["predictions"].values[0]
}
previous_phase_values.update(previous_phase_unadjusted_costs)
# We extract the values for the current phase
# TODO: For things like lighting costs for heating and hot water recommendations, we should actually
# update phase_cost since the phase cost should be the same as the previous phase
@ -507,11 +492,24 @@ class Recommendations:
"sap": phase_energy_efficiency_metrics["sap_change"],
"carbon": phase_energy_efficiency_metrics["carbon_change"],
"heat_demand": phase_energy_efficiency_metrics["heat_demand"],
"unadjusted_heating_cost": phase_cost["heating_cost"]["predictions"].values[0],
"unadjusted_hot_water_cost": phase_cost["hot_water_cost"]["predictions"].values[0],
"unadjusted_lighting_cost": phase_cost["lighting_cost"]["predictions"].values[0]
}
static_cost_variables = (
["epc_heating_cost", "epc_hot_water_cost"] if
rec["type"] == "low_energy_lighting" else ["epc_lighting_cost"]
)
dynamic_cost_variables = [
v for v in ["epc_heating_cost", "epc_hot_water_cost", "epc_lighting_cost"]
if v not in static_cost_variables
]
# Take the static variables from the previous phase
current_phase_costs = {k: v for k, v in previous_phase_values.items() if k in static_cost_variables}
# Insert the dynamic variables from the current phase
for v in dynamic_cost_variables:
current_phase_costs[v] = phase_cost[v.split("epc_")[1]]["adjusted_cost"].values[0]
current_phase_values.update(current_phase_costs)
property_phase_impact = {
# Increasing
"sap": current_phase_values["sap"] - previous_phase_values["sap"],
@ -556,8 +554,10 @@ class Recommendations:
rec["co2_equivalent_savings"] = property_phase_impact["carbon"]
rec["heat_demand"] = property_phase_impact["heat_demand"]
if (rec["sap_points"] is None) and (rec["co2_equivalent_savings"] is None) or (
rec["heat_demand"] is None):
if (
(rec["sap_points"] is None) and (rec["co2_equivalent_savings"] is None) or
(rec["heat_demand"] is None)
):
raise ValueError("sap points, co2 or heat demand is missing")
impact_summary.append(