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adding non-intrusive sap points and survey flag pick up for multiple recommendations

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This commit is contained in:
Khalim Conn-Kowlessar 2024-12-03 19:08:18 +00:00
parent 3e8a1bc4fd
commit 477504abd1
8 changed files with 60 additions and 11 deletions
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12
backend/Property.py
View file

@ -426,6 +426,18 @@ class Property:
if phase_epc_transformation[k] == v:
continue
if k == "hotwater-description":
if (
v == "From main system"
) and (
phase_epc_transformation["mainheat-description"] == "Electric storage heaters"
) and (
"Electric immersion" in phase_epc_transformation["hotwater-description"]
):
# It means we've recommended HHR with electric immersion, and shouldn't overwrite
# the hot water description
continue
raise NotImplementedError(
"Already have this key in the phase_epc_transformation - implement me"
)

1
backend/app/assumptions.py
View file

@ -50,4 +50,5 @@ DESCRIPTIONS_TO_FUEL_TYPES = {
},
"Gas instantaneous at point of use": {"fuel": "Natural Gas", "cop": 0.85},
"Room heaters, wood logs": {"fuel": "Wood Logs", "cop": 1},
"Boiler and radiators, coal": {"fuel": "Coal", "cop": 0.85},
}

11
etl/customers/cottons/remote_assessments.py
View file

@ -40,6 +40,7 @@ def app():
asset_list["uprn"] = asset_list["uprn"].astype(int)
extracted_data = []
model_asset_list = []
for _, home in tqdm(asset_list.iterrows(), total=len(asset_list)):
add1 = home["address1"]
pc = home["postcode"]
@ -63,6 +64,14 @@ def app():
}
)
model_asset_list.append(
{
"uprn": home["uprn"],
"address": epc_searcher.newest_epc["address1"],
"postcode": epc_searcher.newest_epc["postcode"],
}
)
non_invasive_recommendations = [
{
"uprn": r["uprn"],
@ -72,7 +81,7 @@ def app():
filename = f"{USER_ID}/{PORTFOLIO_ID}/asset_list.csv"
save_csv_to_s3(
dataframe=pd.DataFrame(asset_list),
dataframe=pd.DataFrame(model_asset_list),
bucket_name="retrofit-plan-inputs-dev",
file_name=filename
)

5
recommendations/DraughtProofingRecommendations.py
View file

@ -26,6 +26,9 @@ class DraughtProofingRecommendations:
if not draught_proofing_recommendation_config:
return
# Cost is based on a £50 cost per window, based on Checkatrade
cost = draught_proofing_recommendation_config.get("cost", self.property.number_of_windows * 50)
description = (
"Draught proof doors and windows to improve energy efficiency" if
not draught_proofing_recommendation_config.get("description")
@ -48,7 +51,7 @@ class DraughtProofingRecommendations:
"kwh_savings": 0,
"co2_equivalent_savings": 0,
"energy_cost_savings": 0,
"total": draught_proofing_recommendation_config["cost"],
"total": cost,
# We use a very simple and rough estimate of 4 hours per unit
"labour_hours": draught_proofing_recommendation_config.get("labour_hours", 8),
"labour_days": draught_proofing_recommendation_config.get("labour_days", 1), # Assume 8 hour day

13
recommendations/HeatingRecommender.py
View file

@ -1,5 +1,6 @@
import re
import backend.app.assumptions as assumptions
from etl.customers.immo.pilot.asset_list import non_invasive_recommendations
from recommendations.Costs import Costs, BOILER_UPGRADE_SCHEME_ASHP_VALUE
from recommendations.recommendation_utils import (
check_simulation_difference, override_costs, combine_recommendation_configs
@ -981,6 +982,10 @@ class HeatingRecommender:
self.property.data["hot-water-energy-eff"] in ["Very Poor", "Poor", "Average"]
)
non_invasive_recommendation = next((
r for r in self.property.non_invasive_recommendations if r["type"] == "boiler_upgrade"
), {})
if has_inefficient_space_heating or has_inefficient_water:
boiler_size = self.estimate_boiler_size(
property_type=self.property.data["property-type"],
@ -1079,12 +1084,13 @@ class HeatingRecommender:
"description": description,
"starting_u_value": None,
"new_u_value": None,
"sap_points": None,
"sap_points": non_invasive_recommendation.get("sap_points", None),
"already_installed": already_installed,
"simulation_config": simulation_config,
"description_simulation": description_simulation,
**boiler_costs,
"system_type": "boiler_upgrade",
"survey": non_invasive_recommendation.get("survey", None)
}
# We recommend the heating controls
@ -1105,6 +1111,11 @@ class HeatingRecommender:
if not controls_recommender.recommendation and not boiler_recommendation:
return
# If this is true, we set SAP points to None and survey to False for the boiler recommendation
if boiler_recommendation:
boiler_recommendation["sap_points"] = None
boiler_recommendation["survey"] = False
if not system_change and len(boiler_recommendation):
# If there is not a system change, we add the boiler recommendation at point.
self.heating_recommendations.extend([boiler_recommendation])

9
recommendations/HotwaterRecommendations.py
View file

@ -20,6 +20,8 @@ class HotwaterRecommendations:
:return:
"""
# Reset the recommendations
recommendations_phase = phase
self.recommendations = []
non_invasive_recommendations = self.property.non_invasive_recommendations
if non_invasive_recommendations:
@ -28,7 +30,6 @@ class HotwaterRecommendations:
r["type"] in ["hot_water_tank_insulation", "cylinder_thermostat"]
]
recommendations_phase = phase
for m in measures:
non_invasive_rec = [
r for r in non_invasive_recommendations if r["type"] == m
@ -55,7 +56,7 @@ class HotwaterRecommendations:
if self.property.hotwater["clean_description"] == "Gas boiler/circulator, no cylinder thermostat":
# Handle this case specifically:
self.recommend_cylinder_thermostat_gas_boiler_circulator(phase=phase)
self.recommend_cylinder_thermostat_gas_boiler_circulator(phase=recommendations_phase)
return
# If there is no system present, but access to the mains, we
@ -68,14 +69,14 @@ class HotwaterRecommendations:
(self.property.hotwater["no_system_present"] is None) &
(len(has_tank_recommendation) == 0)
):
self.recommend_tank_insulation(phase=phase)
self.recommend_tank_insulation(phase=recommendations_phase)
return
has_cylinder_recommendation = [r for r in self.recommendations if r["type"] == "cylinder_thermostat"]
if ((self.property.hotwater["clean_description"] == "From main system, no cylinder thermostat") &
(len(has_cylinder_recommendation) == 0)):
self.recommend_cylinder_thermostat(phase=phase)
self.recommend_cylinder_thermostat(phase=recommendations_phase)
return
def recommend_tank_insulation(self, phase, sap_points=None, survey=False, _return=False):

10
recommendations/RoofRecommendations.py
View file

@ -290,6 +290,11 @@ class RoofRecommendations:
insulation_materials = pd.DataFrame(insulation_materials)
non_invasive_recommendations = next(
(r for r in self.property.non_invasive_recommendations if
r["type"] == insulation_materials["type"].values[0]), {}
)
lowest_selected_u_value = None
recommendations = []
for _, insulation_material_group in insulation_materials.groupby("description"):
@ -429,14 +434,15 @@ class RoofRecommendations:
"description": self.make_roof_insulation_description(material),
"starting_u_value": u_value,
"new_u_value": new_u_value,
"sap_points": None,
"sap_points": non_invasive_recommendations.get("sap_points", 0),
"already_installed": already_installed,
"simulation_config": simulation_config,
"description_simulation": {
"roof-description": new_description,
"roof-energy-eff": new_efficiency
},
**cost_result
**cost_result,
"survey": non_invasive_recommendations.get("survey", False)
}
)

10
recommendations/WallRecommendations.py
View file

@ -385,6 +385,11 @@ class WallRecommendations(Definitions):
if insulation_thickness == "below average":
cavity_width = cavity_width * (1 - PARTIALLY_FILLED_PERCENTAGE_ASSUMPTION)
non_invasive_recommendations = next(
(r for r in self.property.non_invasive_recommendations if
r["type"] == insulation_materials["type"].values[0]), {}
)
# Test the different fill options
lowest_selected_u_value = None
recommendations = []
@ -475,14 +480,15 @@ class WallRecommendations(Definitions):
"description": description,
"starting_u_value": u_value,
"new_u_value": new_u_value,
"sap_points": None,
"sap_points": non_invasive_recommendations.get("sap_points", None),
"already_installed": already_installed,
"simulation_config": simulation_config,
"description_simulation": {
"walls-description": "Cavity wall, filled cavity",
"walls-energy-eff": "Good"
},
**cost_result
**cost_result,
"survey": non_invasive_recommendations.get("survey", False)
}
)