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implemented change to mechanical ventilation

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This commit is contained in:
Khalim Conn-Kowlessar 2025-03-15 18:25:15 +00:00
parent c2062507ca
commit 91b9530578
3 changed files with 41 additions and 39 deletions
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58
backend/app/plan/router.py
View file

@ -445,6 +445,16 @@ async def trigger_plan(body: PlanTriggerRequest):
bucket_name=get_settings().DATA_BUCKET, file_key="sap_change_model/cleaning_dataset.parquet",
)
# Set up model api and warm up the lambdas
model_api = ModelApi(
portfolio_id=body.portfolio_id,
timestamp=created_at,
prediction_buckets=get_prediction_buckets()
)
await model_api.async_warm_up_lambdas(
model_prefies=model_api.KWH_MODEL_PREFIXES + model_api.MODEL_PREFIXES
)
input_properties = []
for config in tqdm(plan_input):
# We validate each record in the file. If the record is NOT valid, we need to handle this accordingly
@ -539,16 +549,6 @@ async def trigger_plan(body: PlanTriggerRequest):
if not input_properties:
return Response(status_code=204)
# Set up model api and warm up the lambdas
model_api = ModelApi(
portfolio_id=body.portfolio_id,
timestamp=created_at,
prediction_buckets=get_prediction_buckets()
)
await model_api.async_warm_up_lambdas(
model_prefies=model_api.KWH_MODEL_PREFIXES + model_api.MODEL_PREFIXES
)
# The materials data could be cached or local so we don't need to make
# consistent requests to the backend for
# the same data
@ -683,8 +683,6 @@ async def trigger_plan(body: PlanTriggerRequest):
)
# We now insert kwh estimates and costs into the recommendations
# TODO: We should join the methodology which maps the heating and hot water descriptions to the fuel types in
# Recommendations, but also the Property class
logger.info("Calculating tenant savings - kwh and bills")
for property_id in tqdm([p.id for p in input_properties]):
property_recommendations = recommendations.get(property_id, [])
@ -701,8 +699,6 @@ async def trigger_plan(body: PlanTriggerRequest):
property_instance.current_energy_bill = property_current_energy_bill
# Insert the predictions into the recommendations and run the optimiser
# TODO: If a recommendation has a negative impact on SAP, we should remove it - this seems to have become a
# possibility with heating system?
for p in input_properties:
if not recommendations.get(p.id):
@ -814,23 +810,23 @@ async def trigger_plan(body: PlanTriggerRequest):
# Funding
# ~~~~~~~~~~~~~~~~
for p in input_properties:
funding_calulator = Funding(
tenure=body.housing_type,
starting_epc=p.data["current-energy-rating"],
starting_sap=int(p.data["current-energy-efficiency"]),
postcode=p.postcode,
floor_area=p.floor_area,
council_tax_band=None, # This is seemingly always None at the moment
property_recommendations=recommendations[p.id],
project_scores_matrix=eco_project_scores_matrix,
whlg_eligible_postcodes=whlg_eligible_postcodes,
gbis_abs_rate=15,
eco4_abs_rate=15,
)
funding_calulator.check_eligibiltiy()
# Insert finding
p.insert_funding(funding_calulator)
# for p in input_properties:
# funding_calulator = Funding(
# tenure=body.housing_type,
# starting_epc=p.data["current-energy-rating"],
# starting_sap=int(p.data["current-energy-efficiency"]),
# postcode=p.postcode,
# floor_area=p.floor_area,
# council_tax_band=None, # This is seemingly always None at the moment
# property_recommendations=recommendations[p.id],
# project_scores_matrix=eco_project_scores_matrix,
# whlg_eligible_postcodes=whlg_eligible_postcodes,
# gbis_abs_rate=15,
# eco4_abs_rate=15,
# )
# funding_calulator.check_eligibiltiy()
# # Insert finding
# p.insert_funding(funding_calulator)
logger.info("Uploading recommendations to the database")
# If we have any work to do, we create a new scenario

2
recommendations/LightingRecommendations.py
View file

@ -4,6 +4,7 @@ from backend.Property import Property
from typing import List
from recommendations.Costs import Costs
from recommendations.recommendation_utils import override_costs
from backend.ml_models.AnnualBillSavings import AnnualBillSavings
class LightingRecommendations:
@ -161,6 +162,7 @@ class LightingRecommendations:
# the proportion of lights that will be set to low energy
"sap_points": sap_points,
"kwh_savings": heat_demand_change,
"energy_cost_savings": heat_demand_change * AnnualBillSavings.ELECTRICITY_PRICE_CAP,
"co2_equivalent_savings": carbon_change,
"description_simulation": {
"lighting-energy-eff": "Very Good",

20
recommendations/Recommendations.py
View file

@ -858,7 +858,7 @@ class Recommendations:
for recs in property_recommendations:
for rec in recs:
if rec["type"] in [
"mechanical_ventilation", "trickle_vents", "draught_proofing", "extension_cavity_wall_insulation"
"trickle_vents", "draught_proofing", "extension_cavity_wall_insulation"
]:
# We cannot score the impact on draught proofing
continue
@ -883,13 +883,18 @@ class Recommendations:
heating_kwh_savings = (
previous_phase_impact["predictions_heating"].mean() - rec_impact["predictions_heating"].values[0]
)
heating_cost_savings = (
previous_phase_impact["heating_cost"].mean() - rec_impact["heating_cost"].values[0]
)
hotwater_kwh_savings = (
previous_phase_impact["predictions_hotwater"].mean() - rec_impact["predictions_hotwater"].values[0]
)
# Shouldn't be positive
if rec["type"] == "mechanical_ventilation":
heating_kwh_savings = 0 if heating_kwh_savings > 0 else heating_kwh_savings
hotwater_kwh_savings = 0 if hotwater_kwh_savings > 0 else hotwater_kwh_savings
heating_cost_savings = (
previous_phase_impact["heating_cost"].mean() - rec_impact["heating_cost"].values[0]
)
hotwater_host = (
previous_phase_impact["hotwater_cost"].mean() - rec_impact["hotwater_cost"].values[0]
)
@ -897,9 +902,8 @@ class Recommendations:
total_kwh_savings = heating_kwh_savings + hotwater_kwh_savings
energy_cost_savings = heating_cost_savings + hotwater_host
if rec["type"] == "lighting":
# In this case, we should probably just SKIP but check when we have one!
raise Exception("Implement me 3")
if rec["type"] == "low_energy_lighting":
continue
rec["kwh_savings"] = total_kwh_savings
rec["energy_cost_savings"] = energy_cost_savings