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tweaks to router + added tenure filter to epc pipeline

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Khalim Conn-Kowlessar 2024-02-09 17:16:54 +00:00
parent 69d53c85f9
commit 456d0892c0
8 changed files with 108 additions and 53 deletions
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5
backend/Property.py
View file

@ -192,8 +192,9 @@ class Property:
recommendation_record["walls_insulation_thickness_ending"] = "none"
# Update description to indicate it's insulate
if recommendation["type"] in ["solid_floor_insulation", "suspended_floor_insulation",
"exposed_floor_insulation"]:
if recommendation["type"] in [
"solid_floor_insulation", "suspended_floor_insulation", "exposed_floor_insulation"
]:
if len(recommendation["parts"]) > 1:
raise NotImplementedError("Have more than 1 floor insulation part - handle this case")

84
backend/app/plan/router.py
View file

@ -164,6 +164,8 @@ async def trigger_plan(body: PlanTriggerRequest):
model_api = ModelApi(portfolio_id=body.portfolio_id, timestamp=created_at)
# recommendations_scoring_data.loc[17, 'photo_supply_ending'] = 50
all_predictions = model_api.predict_all(
df=recommendations_scoring_data,
bucket=get_settings().DATA_BUCKET,
@ -237,9 +239,10 @@ async def trigger_plan(body: PlanTriggerRequest):
]
recommendations[property_id] = final_recommendations
# impact_df = pd.DataFrame(recommendations[input_properties[0].id])
# This is a temporary step, to estimate the impact of the measured on heat demand and carbon
# TODO: This needs to be cleaned up, if it happens to be kept
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"]]
@ -276,58 +279,26 @@ async def trigger_plan(body: PlanTriggerRequest):
representative_recs[property_id] = default_recommendations
property_instance = [p for p in input_properties if p.id == property_id][0]
recommendation_record = property_instance.base_difference_record.df.to_dict("records")[0].copy()
scoring_dict = {}
for rec in default_recommendations:
scoring_dict = Property.create_recommendation_scoring_data(
property_id=property_instance.id,
recommendation_record=recommendation_record,
recommendation=rec
)
# At each iterations, we update the recommendation record with the changes reflectecd in the
# scoring_dict
for k in scoring_dict.keys():
if k in recommendation_record.keys():
recommendation_record[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)
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
# TODO: The api call producing all_combined_predictions has been removed so we can potentially completely
# refactor this block to just perform the energy adjustments
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 = sum(
x.get("heat_demand", 0) for x in representative_recs[property_id] if
x["type"] not in ["mechanical_ventilation", "low_energy_lighting"]
)
carbon_change = sum(
x.get("co2_equivalent_savings", 0) for x in representative_recs[property_id] if
x["type"] not in ["mechanical_ventilation", "low_energy_lighting"]
)
starting_heat_demand = (
float(property_instance.data["energy-consumption-current"]) * property_instance.floor_area
)
expected_heat_demand = starting_heat_demand - (
combined_heat_demand["predictions"].values[0] * property_instance.floor_area
)
expected_heat_demand = starting_heat_demand - heat_demand_change
# We don't want to adjust the heat demand for mechanical ventilation so we add it back on
@ -361,6 +332,21 @@ async def trigger_plan(body: PlanTriggerRequest):
in representative_recs[property_id]
]
representative_rec_data = pd.DataFrame(representative_rec_data)
# Suppress mechanical ventilation to have zero heat demand and co2
representative_rec_data.loc[
representative_rec_data["type"] == "mechanical_ventilation", "co2_equivalent_savings"
] = 0
representative_rec_data.loc[
representative_rec_data["type"] == "mechanical_ventilation", "heat_demand"
] = 0
# Supress low energy lighting to have zero heat demand and co2 - this does not get affected by this process
representative_rec_data.loc[
representative_rec_data["type"] == "low_energy_lighting", "co2_equivalent_savings"
] = 0
representative_rec_data.loc[
representative_rec_data["type"] == "low_energy_lighting", "heat_demand"
] = 0
# Convert co2 and heat demand to proportions of their column sums
representative_rec_data["co2_equivalent_savings_percent"] = (
representative_rec_data["co2_equivalent_savings"] /
@ -393,10 +379,18 @@ async def trigger_plan(body: PlanTriggerRequest):
rec["co2_equivalent_savings"] = 0
rec["heat_demand"] = 0
rec["energy_cost_savings"] = 0
elif rec["type"] == "low_energy_lighting":
# We do not convert, we just calculate energy cost savings
rec["energy_cost_savings"] = AnnualBillSavings.estimate_electric(rec["heat_demand"])
continue
else:
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"])
# If the recommendation is solar, the savings are entirely in electricity
if rec["type"] == "solar_pv":
rec["energy_cost_savings"] = AnnualBillSavings.estimate_electric(rec["heat_demand"])
else:
rec["energy_cost_savings"] = AnnualBillSavings.estimate(rec["heat_demand"])
# Update recommendations
recommendations[property_id] = property_recommendations

9
backend/ml_models/AnnualBillSavings.py
View file

@ -29,6 +29,15 @@ class AnnualBillSavings:
"""
return cls.PRICE_FACTOR * kwh
@classmethod
def estimate_electric(cls, kwh: float):
"""
Estimate the annual bill savings based on the kwh savings
:param kwh: The kwh savings
:return: An estimate for annual bill savings
"""
return cls.ELECTRICITY_PRICE_CAP * kwh
@classmethod
def adjust_energy_to_metered(cls, epc_energy_consumption, current_epc_rating):
"""

7
etl/epc/DataProcessor.py
View file

@ -8,6 +8,7 @@ from etl.epc.settings import (
IGNORED_TRANSACTION_TYPES,
IGNORED_FLOOR_LEVELS,
IGNORED_PROPERTY_TYPES,
IGNORED_TENURES,
FULLY_GLAZED_DESCRIPTIONS,
AVERAGE_FIXED_FEATURES,
BUILT_FORM_REMAP,
@ -632,6 +633,7 @@ class EPCDataProcessor:
violation_missing_hotwater_description = pd.isnull(self.data["HOTWATER_DESCRIPTION"])
violation_missing_roof_description = pd.isnull(self.data["ROOF_DESCRIPTION"])
violation_invalid_property_type = self.data["PROPERTY_TYPE"] == IGNORED_PROPERTY_TYPES
violation_invalid_tenure = self.data["TENURE"].isin(IGNORED_TENURES)
violation_df = pd.concat(
[
@ -644,6 +646,7 @@ class EPCDataProcessor:
violation_missing_hotwater_description,
violation_missing_roof_description,
violation_invalid_property_type,
violation_invalid_tenure,
], axis=1,
keys=[
"violation_uprn_missing",
@ -655,6 +658,7 @@ class EPCDataProcessor:
"violation_missing_hotwater_description",
"violation_missing_roof_description",
"violation_invalid_property_type",
"violation_invalid_tenure"
]
)
@ -697,6 +701,9 @@ class EPCDataProcessor:
# EPCs) we'll ignore them from the model
self.data = self.data[self.data["PROPERTY_TYPE"] != IGNORED_PROPERTY_TYPES]
# We remove EPCs where the tenure is unknown, but is usually an indicator of a new build
self.data = self.data[self.data["TENURE"] != IGNORED_TENURES]
def clean_multi_glaze_proportion(self, ignore_step: bool = False) -> None:
"""
If there is no multi-glaze proportion but the windows are fully glazed, then we should assume a score of 100

4
etl/epc/settings.py
View file

@ -215,6 +215,10 @@ EARLIEST_EPC_DATE = "2014-08-01"
IGNORED_TRANSACTION_TYPES = "new dwelling"
IGNORED_FLOOR_LEVELS = ["top floor", "mid floor"]
IGNORED_PROPERTY_TYPES = "Park home"
IGNORED_TENURES = [
"Not defined - use in the case of a new dwelling for which the intended tenure in not known. It is not to be used "
"for an existing dwelling"
]
RDSAP_RESPONSE = "CURRENT_ENERGY_EFFICIENCY"
HEAT_DEMAND_RESPONSE = "ENERGY_CONSUMPTION_CURRENT"

6
etl/testing_data/retrofitted_properties.py
View file

@ -18,6 +18,12 @@ def app():
test_file = pd.DataFrame(
[
# Live West Properties
{"address": "42, Foxes Field", "postcode": "TR18 3RJ", "Notes": None},
{"address": "11, Cranley Gardens", "postcode": "TQ13 8UT", "Notes": None},
# Keyzy properties
{'address': '2 South Terrace', 'postcode': 'NN1 5JY', 'Notes': ''},
{'address': '25 Albert Street', 'postcode': 'PO12 4TY', 'Notes': ''},
# Pilot properties
{'address': '113 Tenby Road', 'postcode': 'B13 9LT', 'Notes': ''},
{'address': '139 School Road', 'postcode': 'B28 8JF', 'Notes': ''},

32
recommendations/LightingRecommendations.py
View file

@ -23,6 +23,34 @@ class LightingRecommendations:
self.material = material[0]
self.recommendation = []
@staticmethod
def estimate_lighting_impact(number_of_bulbs: int):
"""
Placeholder function to estimate the actual energy savings of LEDs vs traditional lighting
:return:
"""
wattage_incandescent = 60 # wattage of typical incandescent bulb in watts
wattage_led = 10 # wattage of typical LED bulb in watts
hours_per_day = 3 # average usage in hours per day
days_per_year = 365 # days in a year
national_grid_carbon_intensity = 162 # gCO2/kWh, average for 2023 in the UK
# Energy usage per year for incandescent and LED bulbs (in kWh)
energy_usage_incandescent_per_year = (wattage_incandescent / 1000) * hours_per_day * days_per_year
energy_usage_led_per_year = (wattage_led / 1000) * hours_per_day * days_per_year
# Energy savings per bulb per year
energy_savings_per_bulb_per_year = energy_usage_incandescent_per_year - energy_usage_led_per_year
# Total energy savings for all bulbs
total_energy_savings_per_year = energy_savings_per_bulb_per_year * number_of_bulbs
carbon_reduction_grams = total_energy_savings_per_year * national_grid_carbon_intensity
carbon_reduction_tonnes = carbon_reduction_grams / 1_000_000 # converting grams to tonnes
return total_energy_savings_per_year, carbon_reduction_tonnes
def recommend(self):
"""
This method will check if there are any lighting fittings that aren't low energy.
@ -58,6 +86,8 @@ class LightingRecommendations:
else:
description = "Install low energy lighting in %s outlets" % str(number_non_lel_outlets)
heat_demand_change, carbon_change = self.estimate_lighting_impact(number_non_lel_outlets)
self.recommendation = [
{
"parts": [],
@ -68,6 +98,8 @@ class LightingRecommendations:
# For SAP points, we use the fact that lighting is usually worth 2 points and we scale this to
# the proportion of lights that will be set to low energy
"sap_points": round(2 * (number_non_lel_outlets / number_lighting_outlets), 2),
"heat_demand": heat_demand_change,
"co2_equivalent_savings": carbon_change,
**cost_result
}
]

14
recommendations/Recommendations.py
View file

@ -143,6 +143,10 @@ class Recommendations:
for recommendations_by_type in property_recommendations:
for rec in recommendations_by_type:
# We don't use the model for low energy lighting at the moment
if rec["type"] == "low_energy_lighting":
continue
new_heat_demand = property_heat_predictions[property_heat_predictions["recommendation_id"] == str(
rec["recommendation_id"]
)]["predictions"].values[0]
@ -151,12 +155,10 @@ class Recommendations:
rec["recommendation_id"]
)]["predictions"].values[0]
# We don't use the model for low energy lighting at the moment
if rec["type"] != "low_energy_lighting":
new_sap = property_sap_predictions[property_sap_predictions["recommendation_id"] == str(
rec["recommendation_id"]
)]["predictions"].values[0]
rec["sap_points"] = new_sap - float(property_instance.data["current-energy-efficiency"])
new_sap = property_sap_predictions[property_sap_predictions["recommendation_id"] == str(
rec["recommendation_id"]
)]["predictions"].values[0]
rec["sap_points"] = new_sap - float(property_instance.data["current-energy-efficiency"])
if rec["type"] == "mechanical_ventilation":
# For the moment, we cap the number of SAP points that can be achieved by ventilation at 2