junte/Model
1
0
Fork 0
mirror of https://github.com/Hestia-Homes/Model.git synced 2026-06-08 11:17:27 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

inserting the kwh savings into the recommendation

Browse source
This commit is contained in:
Khalim Conn-Kowlessar 2024-08-08 11:10:21 +01:00
parent a2834a180e
commit c77f642861
2 changed files with 87 additions and 13 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

26
backend/Property.py
View file

@ -430,28 +430,28 @@ class Property:
if self.simulation_epcs is None:
raise ValueError("Simulation EPCs have not been created")
phases = sorted(list(self.simulation_epcs.keys()))
rec_ids = sorted(list(self.simulation_epcs.keys()))
updated_simulation_epcs = []
for phase in phases:
sim_epc = self.simulation_epcs[phase].copy()
phase_impact = [x for x in impact_summary if x["phase"] == phase][0]
for rec_id in rec_ids:
sim_epc = self.simulation_epcs[rec_id].copy()
rec_impact = [x for x in impact_summary if x["recommendation_id"] == rec_id][0]
# We update all of the features that should have an impact on the kwh model
sim_epc.update(
{
"heating-cost-current": phase_impact["epc_heating_cost"],
"hot-water-cost-current": phase_impact["epc_hot_water_cost"],
"heating-cost-current": rec_impact["epc_heating_cost"],
"hot-water-cost-current": rec_impact["epc_hot_water_cost"],
# CO₂ emissions per square metre floor area per year in kg/m². Since CO₂ emissions are in tonnes
# per year, we multiply by 1000 to get kg/m²
"co2-emiss-curr-per-floor-area": round(
1000 * (phase_impact["carbon"] / self.data["total-floor-area"])
1000 * (rec_impact["carbon"] / self.data["total-floor-area"])
),
"co2-emissions-current": phase_impact["carbon"],
"current-energy-rating": sap_to_epc(phase_impact["sap"]),
"current-energy-efficiency": int(np.floor(phase_impact["sap"])),
"energy-consumption-current": phase_impact["heat_demand"],
"lighting-cost-current": phase_impact["epc_lighting_cost"],
"phase": phase
"co2-emissions-current": rec_impact["carbon"],
"current-energy-rating": sap_to_epc(rec_impact["sap"]),
"current-energy-efficiency": int(np.floor(rec_impact["sap"])),
"energy-consumption-current": rec_impact["heat_demand"],
"lighting-cost-current": rec_impact["epc_lighting_cost"],
"id": "+".join([str(self.id), rec_id])
}
)
updated_simulation_epcs.append(sim_epc)

74
backend/app/plan/router.py
View file

@ -874,6 +874,80 @@ async def trigger_plan(body: PlanTriggerRequest):
# We call the API with the scoring epcs
scoring_epcs = pd.DataFrame(scoring_epcs)
scoring_epcs = add_features_from_code(scoring_epcs)
scoring_epcs = add_estimate_annual_kwh(scoring_epcs)
# TODO: Drop all potential and env columns
kwh_simulation_predictions = model_api.predict_all(
df=scoring_epcs,
bucket=get_settings().DATA_BUCKET,
prediction_buckets=get_prediction_buckets(),
model_prefixes=["heating_kwh_predictions", "hotwater_kwh_predictions"],
extract_ids=True
)
# We now insert into the recommendations
for property_id in recommendations.keys():
property_recommendations = recommendations[property_id]
property_instance = [p for p in input_properties if p.id == property_id][0]
# The predicted kwhs are without appliances
consumption = property_instance.energy_consumption_estimates["adjusted"]
# Starting consumption is the sum of the consumption values, without appliances
starting_heating = consumption["heating"]
starting_hotwater = consumption["hot_water"]
property_kwh_predictions = {
k: kwh_simulation_predictions[k][kwh_simulation_predictions[k]["property_id"] == str(property_id)]
for k in ['heating_kwh_predictions', 'hotwater_kwh_predictions']
}
# We adjust the predictions
from backend.ml_models.AnnualBillSavings import AnnualBillSavings
for k in ["heating_kwh_predictions", "hotwater_kwh_predictions"]:
property_kwh_predictions[k]["adjusted"] = property_kwh_predictions[k]["predictions"].apply(
lambda x: AnnualBillSavings.adjust_energy_to_metered(
epc_energy=x, current_epc_rating=property_instance.data["current-energy-rating"]
)
)
# For each recommendation, we difference the predictions
property_kwh_predictions["heating_kwh_predictions"]["savings"] = np.diff(
property_kwh_predictions["heating_kwh_predictions"]["adjusted"], prepend=starting_heating
)
property_kwh_predictions["hotwater_kwh_predictions"]["savings"] = np.diff(
property_kwh_predictions["hotwater_kwh_predictions"]["adjusted"], prepend=starting_hotwater
)
for recommendations_by_type in property_recommendations:
for rec in recommendations_by_type:
# In the case of mechanical ventilation, there is no impact, and for low energy lighting we
# calculate the savings inside of the recommendation itself
if rec["type"] in ["mechanical_ventilation", "low_energy_lighing"]:
continue
heating_kwh_savings = property_kwh_predictions["heating_kwh_predictions"][
(
property_kwh_predictions["heating_kwh_predictions"]["recommendation_id"] ==
rec["recommendation_id"]
)
]["savings"].values[0]
# This should be negative
if heating_kwh_savings > 0:
print("Positive heating kwh savings")
# TODO: Raise an exception to investigate
# raise Exception("Positive heating kwh savings")
hot_water_kwh_savings = property_kwh_predictions["hotwater_kwh_predictions"][
(
property_kwh_predictions["hotwater_kwh_predictions"]["recommendation_id"] ==
rec["recommendation_id"]
)
]["savings"].values[0]
# This should be negative
if hot_water_kwh_savings > 0:
print("Positive hot water kwh savings")
# TODO: Raise an exception to investigate
# raise Exception("Positive hot water kwh savings")
rec["kwh_savings"] = abs(heating_kwh_savings + hot_water_kwh_savings)
# 1) the property data
# 2) the property details (epc)