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Put in placeholder method to break down carbon and energy savings

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This commit is contained in:
Khalim Conn-Kowlessar 2023-11-28 22:32:12 +00:00
parent 7ad88efbd4
commit d97a91eec7
4 changed files with 170 additions and 51 deletions
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9
backend/app/db/functions/portfolio_functions.py
View file

@ -3,7 +3,9 @@ from backend.app.db.models.recommendations import Plan, PlanRecommendations, Rec
from backend.app.db.models.portfolio import Portfolio
def aggregate_portfolio_recommendations(session, portfolio_id: int, total_valuation_increase: float):
def aggregate_portfolio_recommendations(
session, portfolio_id: int, total_valuation_increase: float, labour_days: float
):
# Aggregate multiple fields
aggregates = (
session.query(
@ -12,7 +14,6 @@ def aggregate_portfolio_recommendations(session, portfolio_id: int, total_valuat
func.sum(Recommendation.heat_demand).label("energy_savings"),
func.sum(Recommendation.co2_equivalent_savings).label("co2_equivalent_savings"),
func.sum(Recommendation.energy_cost_savings).label("energy_cost_savings"),
func.sum(Recommendation.labour_days).label("labour_days"),
)
.join(PlanRecommendations, PlanRecommendations.recommendation_id == Recommendation.id)
.join(Plan, Plan.id == PlanRecommendations.plan_id)
@ -26,7 +27,6 @@ def aggregate_portfolio_recommendations(session, portfolio_id: int, total_valuat
"energy_savings": aggregates.energy_savings or 0,
"co2_equivalent_savings": aggregates.co2_equivalent_savings or 0,
"energy_cost_savings": aggregates.energy_cost_savings or 0,
"labour_days": aggregates.labour_days or 0,
}
# Get the portfolio and update the fields
@ -35,8 +35,9 @@ def aggregate_portfolio_recommendations(session, portfolio_id: int, total_valuat
for key, value in aggregates_dict.items():
setattr(portfolio, key, value)
# Insert total valuation increase
# Insert total valuation increase and labour days
portfolio.property_valuation_increase = total_valuation_increase
portfolio.labour_days = labour_days
# Merge the updated portfolio back into the session
session.merge(portfolio)

175
backend/app/plan/router.py
View file

@ -1,5 +1,6 @@
from datetime import datetime
import numpy as np
import pandas as pd
from epc_api.client import EpcClient
from fastapi import APIRouter, Depends
@ -34,6 +35,7 @@ from recommendations.Recommendations import Recommendations
from utils.logger import setup_logger
from utils.s3 import read_dataframe_from_s3_parquet
from backend.ml_models.Valuation import PropertyValuation
from backend.ml_models.AnnualBillSavings import AnnualBillSavings
logger = setup_logger()
@ -118,6 +120,7 @@ async def trigger_plan(body: PlanTriggerRequest):
recommendations = {}
recommendations_scoring_data = []
property_scoring_data = {}
for p in input_properties:
@ -156,6 +159,12 @@ async def trigger_plan(body: PlanTriggerRequest):
# We update the ending record with the recommended updates and we set lodgement date to today
ending_epc_data["DAYS_TO_ENDING"] = data_processor.calculate_days_to(created_at)
property_scoring_data[p.id] = {
"starting_epc_data": starting_epc_data,
"ending_epc_data": ending_epc_data,
"fixed_data": fixed_data
}
for recommendations_by_type in property_recommendations:
for i, rec in enumerate(recommendations_by_type):
scoring_dict = create_recommendation_scoring_data(
@ -219,22 +228,6 @@ async def trigger_plan(body: PlanTriggerRequest):
recommendations=recommendations
)
print("GET RID OF ME!")
rec_df = []
for rec_group in recommendations_with_impact:
for rec in rec_group:
rec_df.append(
{
"type": rec["type"],
"description": rec["description"],
"sap": rec["sap_points"],
"total": rec["total"],
"co2_equivalent_savings": rec["co2_equivalent_savings"],
"heat_demand": rec["heat_demand"]
}
)
rec_df = pd.DataFrame(rec_df)
input_measures = prepare_input_measures(recommendations_with_impact, body.goal)
if body.budget:
@ -270,6 +263,148 @@ async def trigger_plan(body: PlanTriggerRequest):
]
recommendations[property_id] = final_recommendations
# This is a temporary step, to estimate the impact of the measured on heat demand and carbon
combined_recommendations_scoring_data = []
representative_recs = {}
for property_id, property_recommendations in recommendations.items():
default_recommendations = [r for r in property_recommendations if r["default"]]
default_types = {x["type"] for x in default_recommendations}
# Missing types
missing_types = list(set([r["type"] for r in property_recommendations if r["type"] not in default_types]))
if missing_types:
for missed_type in missing_types:
missed = [r for r in property_recommendations if r["type"] == missed_type]
median_cost = np.median([r["total"] for r in missed])
# Grab a representative, based on median cost
representative_rec = [r for r in property_recommendations if r["total"] == median_cost]
default_recommendations.append(representative_rec[0])
representative_recs[property_id] = default_recommendations
property_instance = [p for p in input_properties if p.id == property_id][0]
property_scoring_datasets = property_scoring_data[property_id]
starting_epc_data = property_scoring_datasets["starting_epc_data"].copy()
ending_epc_data = property_scoring_datasets["ending_epc_data"].copy()
fixed_data = property_scoring_datasets["fixed_data"].copy()
scoring_dict = {}
for rec in default_recommendations:
scoring_dict = create_recommendation_scoring_data(
property=property_instance,
recommendation=rec,
starting_epc_data=starting_epc_data,
ending_epc_data=ending_epc_data,
fixed_data=fixed_data,
)
# At each iteration, we want to update the ending_epc_data, so in the end, ending_epc_data contains
# all of the updates
for k in scoring_dict.keys():
if k in ending_epc_data.columns:
ending_epc_data[k] = scoring_dict[k]
combined_recommendations_scoring_data.append(scoring_dict)
# PERFORM SAME STEPS AGAIN - TODO: TO BE REMOVED
combined_recommendations_scoring_data = pd.DataFrame(combined_recommendations_scoring_data)
# Perform the same cleaning as in the model - first clean number of room variables though
combined_recommendations_scoring_data = DataProcessor.apply_averages_cleaning(
data_to_clean=combined_recommendations_scoring_data,
cleaning_data=cleaning_data,
cols_to_merge_on=['PROPERTY_TYPE', 'BUILT_FORM', 'CONSTRUCTION_AGE_BAND', 'LOCAL_AUTHORITY'],
colnames=["NUMBER_HABITABLE_ROOMS", "NUMBER_HEATED_ROOMS"],
)
combined_recommendations_scoring_data = DataProcessor.apply_averages_cleaning(
data_to_clean=combined_recommendations_scoring_data,
cleaning_data=cleaning_data,
cols_to_merge_on=COLUMNS_TO_MERGE_ON + ["LOCAL_AUTHORITY"],
).drop(columns=["LOCAL_AUTHORITY"])
combined_recommendations_scoring_data = DataProcessor.clean_missings_after_description_process(
combined_recommendations_scoring_data,
ignore_cols=[c for c in combined_recommendations_scoring_data.columns if ("thermal_transmittance" in c) or (
"insulation_thickness" in c) or ("ENERGY_EFF" in c)]
)
combined_recommendations_scoring_data = DataProcessor.clean_efficiency_variables(
combined_recommendations_scoring_data
)
model_api = ModelApi(portfolio_id=body.portfolio_id, timestamp=created_at)
all_combined_predictions = model_api.predict_all(
df=combined_recommendations_scoring_data,
bucket=get_settings().DATA_BUCKET,
prediction_buckets={
"sap_change_predictions": get_settings().SAP_PREDICTIONS_BUCKET,
"heat_demand_predictions": get_settings().HEAT_PREDICTIONS_BUCKET,
"carbon_change_predictions": get_settings().CARBON_PREDICTIONS_BUCKET
}
)
# We update the carbon and heat demand predictions
for property_id, property_recommendations in recommendations.items():
combined_heat_demand = all_combined_predictions["heat_demand_predictions"]
combined_heat_demand = combined_heat_demand[combined_heat_demand["property_id"] == str(property_id)]
combined_carbon = all_combined_predictions["carbon_change_predictions"]
combined_carbon = combined_carbon[combined_carbon["property_id"] == str(property_id)]
property_instance = [p for p in input_properties if p.id == property_id][0]
carbon_change = float(
property_instance.data["co2-emissions-current"]
) - combined_carbon["predictions"].values[0]
heat_demand_change = (
(float(property_instance.data["energy-consumption-current"]) -
combined_heat_demand["predictions"].values[0])
* property_instance.floor_area
)
# update the recommendations
# We need to totals for the representative recommendations
representative_rec_data = [
{
"recommendation_id": r["recommendation_id"],
"co2_equivalent_savings": r["co2_equivalent_savings"],
"heat_demand": r["heat_demand"],
"type": r["type"]
} for r
in representative_recs[property_id]
]
representative_rec_data = pd.DataFrame(representative_rec_data)
# Convert co2 and heat demand to proportions of their column sums
representative_rec_data["co2_equivalent_savings_percent"] = (
representative_rec_data["co2_equivalent_savings"] /
representative_rec_data["co2_equivalent_savings"].sum()
)
representative_rec_data["heat_demand_percent"] = (
representative_rec_data["heat_demand"] / representative_rec_data["heat_demand"].sum()
)
# We'll use the proportions to update the carbon and heat demand
representative_rec_data["co2_equivalent_savings"] = (
carbon_change * representative_rec_data["co2_equivalent_savings_percent"]
)
representative_rec_data["heat_demand"] = (
heat_demand_change * representative_rec_data["heat_demand_percent"]
)
# Finally, insert these values into the final recommendations
for rec in property_recommendations:
change_data = representative_rec_data[representative_rec_data["type"] == rec["type"]]
rec["co2_equivalent_savings"] = change_data["co2_equivalent_savings"].values[0]
rec["heat_demand"] = change_data["heat_demand"].values[0]
rec["energy_cost_savings"] = AnnualBillSavings.estimate(rec["heat_demand"])
# Update recommendations
recommendations[property_id] = property_recommendations
# 1) the property data
# 2) the property details (epc)
# 3) the recommendations
@ -342,9 +477,15 @@ async def trigger_plan(body: PlanTriggerRequest):
# the portfolion level impact
total_valuation_increase = sum(property_valuation_increases)
labour_days = max(
[sum(r["labour_days"] for r in rec_group if r["default"]) for p_id, rec_group in recommendations.items()]
)
aggregate_portfolio_recommendations(
session, portfolio_id=body.portfolio_id, total_valuation_increase=total_valuation_increase
session,
portfolio_id=body.portfolio_id,
total_valuation_increase=total_valuation_increase,
labour_days=labour_days
)
# Commit final changes

35
backend/ml_models/Valuation.py
View file

@ -4,42 +4,19 @@ class PropertyValuation:
"""
UPRN_VALUE_LOOKUP = {
15038202: 202000,
37024763: 213000,
15038202: {"current_value": 202000, "increase_percentage": 0.05725},
37024763: {"current_value": 213000, "increase_percentage": 0.03625},
}
VALUE_INCREASE_MAPPING = [
{
"starting_epc": "D",
"ending_epc": "C",
"increase_percentage": 0.03625,
},
{
"starting_epc": "D",
"ending_epc": "B",
"increase_percentage": 0.05725,
},
]
@classmethod
def estimate(cls, property_instance, target_epc):
current_value = cls.UPRN_VALUE_LOOKUP.get(property_instance.uprn)
data = cls.UPRN_VALUE_LOOKUP.get(property_instance.uprn)
raise ValueError("NEED TO UPDATE THIS")
if not current_value:
if not data:
raise ValueError("Have not implemented valuation for this property")
valuation_increases = [
v for v in cls.VALUE_INCREASE_MAPPING if
v["starting_epc"] == property_instance.data["current-energy-rating"] and v["ending_epc"] == target_epc
]
new_valuation = (1 + data["increase_percentage"]) * data["current_value"]
if len(valuation_increases) != 1:
raise ValueError("Valuation increase mapping not found")
new_valuation = (1 + valuation_increases[0]["increase_percentage"]) * current_value
increase = round(new_valuation - current_value, 2)
increase = round(new_valuation - data["current_value"], 2)
return increase

2
recommendations/LightingRecommendations.py
View file

@ -54,7 +54,7 @@ class LightingRecommendations:
)
if number_non_lel_outlets == 1:
description = "Install low energy lighting in %s 1 remaining outlet"
description = "Install low energy lighting in 1 remaining outlet"
else:
description = "Install low energy lighting in %s outlets" % str(number_non_lel_outlets)