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Added cavity extraction and re-fill recommendation and costing

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This commit is contained in:
Khalim Conn-Kowlessar 2024-04-16 13:21:14 +01:00
parent 0c1fb0360f
commit 4cf4d67ac9
5 changed files with 63 additions and 17 deletions
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17
backend/Property.py
View file

@ -61,7 +61,7 @@ class Property:
n_bedrooms = None
def __init__(
self, id, postcode, address, epc_record, already_installed=None, property_non_invasive_recommendations=None,
self, id, postcode, address, epc_record, already_installed=None, non_invasive_recommendations=None,
**kwargs
):
@ -82,8 +82,8 @@ class Property:
self.already_installed = ast.literal_eval(already_installed['already_installed']) if already_installed else []
self.non_invasive_recommendations = (
ast.literal_eval(property_non_invasive_recommendations['recommendations']) if
property_non_invasive_recommendations else []
ast.literal_eval(non_invasive_recommendations['recommendations']) if
non_invasive_recommendations else []
)
self.uprn = epc_record.get("uprn")
@ -284,6 +284,7 @@ class Property:
recommendation_record=recommendation_record,
recommendations=previous_phase_representatives + [rec],
primary_recommendation_id=rec["recommendation_id"],
non_invasive_recommendations=self.non_invasive_recommendations,
)
self.recommendations_scoring_data.append(scoring_dict)
@ -293,6 +294,7 @@ class Property:
recommendation_record,
recommendations: list,
primary_recommendation_id: int,
non_invasive_recommendations: list = None,
):
"""
This function will iterate through a list of recommendations and apply a simulation for each recommendation
@ -301,10 +303,12 @@ class Property:
:param recommendation_record: The record of the property, which will be updated
:param recommendations: The list of recommendations to apply
:param primary_recommendation_id: The id of the primary recommendation, which is used to identify the record
:param non_invasive_recommendations: The list of non-invasive recommendations
:return: The updated recommendation record
"""
output = recommendation_record.copy()
non_invasive_recommendations = [] if non_invasive_recommendations is None else non_invasive_recommendations
for col in [
"walls_insulation_thickness",
@ -323,6 +327,13 @@ class Property:
"external_wall_insulation",
"cavity_wall_insulation",
]:
# # If we have a non-incasive recommendation that the cavity wall is partially filled, we skip the
# # cavity wall insulation recommendation (since on the EPC, the property will look like how it did
# # before any works)
# if "cavity_surveyed_as_filled_is_partial" in non_invasive_recommendations:
# continue
# The upgrade made here is to the u-value of the walls and the description of the
# insulation thickness
output["walls_thermal_transmittance_ending"] = recommendation[

14
backend/app/plan/router.py
View file

@ -171,11 +171,13 @@ def extract_portfolio_aggregation_data(
def format_money(amount):
return f"£{amount:,.0f}"
valuation_improvment_per_unit = format_money(
total_valuation_increase / n_units) + (f" ({format_money(valuation_improvement_lower_bound_per_unit)} - "
f"{format_money(valuation_improvement_upper_bound_per_unit)})")
valuation_improvment_per_unit = str(
format_money(
total_valuation_increase / n_units) + (f" ({format_money(valuation_improvement_lower_bound_per_unit)} - "
f"{format_money(valuation_improvement_upper_bound_per_unit)})")
)
valuation_return_on_investment = (
valuation_return_on_investment = str(
str(round(total_valuation_increase / agg_data["cost"].sum(), 2)) +
f" ("
f"{agg_data['lower_bound_valuation_uplift'].sum() / agg_data['cost'].sum():,.2f} - "
@ -189,8 +191,8 @@ def extract_portfolio_aggregation_data(
"epc_breakdown_post_retrofit": json.dumps(
reformat_epc_data(agg_data["post_retrofit_epc"].value_counts().to_dict())
),
"number_of_properties": n_units,
"n_units_to_retrofit": n_units_to_retrofit,
"number_of_properties": int(n_units),
"n_units_to_retrofit": int(n_units_to_retrofit),
"co2_per_unit_pre_retrofit": str(round(agg_data["pre_retrofit_co2"].mean(), 2)) + "t",
"co2_per_unit_post_retrofit": str(round(agg_data["post_retrofit_co2"].mean(), 2)) + "t",
"energy_bill_per_unit_pre_retrofit": format_money(agg_data["pre_retrofit_energy_bill"].mean()),

13
recommendations/Costs.py
View file

@ -91,6 +91,10 @@ DOUBLE_RADIATOR_COST = 300
FLUE_COST = 600
PIPEWORK_COST = 750 # Min cost is £500
# This is the cost per meter squared for cavity extraction
# https://www.checkatrade.com/blog/cost-guides/cavity-wall-insulation-removal-cost/
CAVITY_EXTRACTION_COST = 21.5
class Costs:
"""
@ -173,7 +177,7 @@ class Costs:
if not self.labour_adjustment_factor:
raise ValueError("Labour adjustment factor not found")
def cavity_wall_insulation(self, wall_area, material):
def cavity_wall_insulation(self, wall_area, material, is_extraction_and_refill=False):
"""
Calculates the total cost for cavity wall insulation based on material and labor costs,
including contingency, preliminaries, profit, and VAT.
@ -208,6 +212,13 @@ class Costs:
# Assume a team of 2
labour_days = (labour_hours / 8) / 2
if is_extraction_and_refill:
# bump up the cost of the work
total_cost = total_cost + CAVITY_EXTRACTION_COST * wall_area
# Additional 2 days work
labour_hours = labour_hours + (2 * 8)
labour_days = labour_days + 2
return {
"total": total_cost,
"subtotal": subtotal_before_vat,

19
recommendations/Recommendations.py
View file

@ -149,12 +149,14 @@ class Recommendations:
property_recommendations = self.insert_temp_recommendation_id(property_recommendations)
# We also need to create the representative recommendations for each recommendation type
property_representative_recommendations = self.create_representative_recommendations(property_recommendations)
property_representative_recommendations = self.create_representative_recommendations(
property_recommendations, non_invasive_recommendations=self.property_instance.non_invasive_recommendations
)
return property_recommendations, property_representative_recommendations
@staticmethod
def create_representative_recommendations(property_recommendations):
def create_representative_recommendations(property_recommendations, non_invasive_recommendations):
"""
This method will create a representative recommendation for each recommendation type
In order to create a representative recommendation, we choose the recommendation that has:
@ -169,6 +171,13 @@ class Recommendations:
for recommendations_by_type in property_recommendations:
# If the property was initially surveyed as filled, but the cavity was only partially filled, we don't
# want to include the cavity wall insulation recommendation in the defaults
# if (recommendations_by_type[0].get("type") == "cavity_wall_insulation") and (
# "cavity_surveyed_as_filled_is_partial" in non_invasive_recommendations
# ):
# continue
if recommendations_by_type[0].get("type") == "mechanical_ventilation":
continue
@ -238,13 +247,13 @@ class Recommendations:
property_sap_predictions = all_predictions["sap_change_predictions"][
all_predictions["sap_change_predictions"]["property_id"] == str(property_instance.id)
]
].copy()
property_heat_predictions = all_predictions["heat_demand_predictions"][
all_predictions["heat_demand_predictions"]["property_id"] == str(property_instance.id)
]
].copy()
property_carbon_predictions = all_predictions["carbon_change_predictions"][
all_predictions["carbon_change_predictions"]["property_id"] == str(property_instance.id)
]
].copy()
property_recommendations = recommendations[property_instance.id].copy()

17
recommendations/WallRecommendations.py
View file

@ -113,7 +113,9 @@ class WallRecommendations(Definitions):
insulation_thickness = self.property.walls["insulation_thickness"]
# We check if the wall is already insulated and if so, we exit
if (insulation_thickness in ["average", "above average"]) or self.property.walls["is_filled_cavity"]:
if ((insulation_thickness in ["average", "above average"]) or self.property.walls["is_filled_cavity"]) and (
"cavity_extract_and_refill" not in self.property.non_invasive_recommendations
):
return
if u_value:
@ -216,15 +218,26 @@ class WallRecommendations(Definitions):
if new_u_value <= self.BUILDING_REGULATIONS_PART_L_CAVITY_WALL_MAX_U_VALUE:
lowest_selected_u_value = update_lowest_selected_u_value(lowest_selected_u_value, new_u_value)
is_extraction_and_refill = "cavity_extract_and_refill" in self.property.non_invasive_recommendations
cost_result = self.costs.cavity_wall_insulation(
wall_area=self.property.insulation_wall_area,
material=material.to_dict(),
is_extraction_and_refill=is_extraction_and_refill
)
already_installed = "cavity_wall_insulation" in self.property.already_installed
if already_installed:
cost_result = override_costs(cost_result)
if is_extraction_and_refill:
description = f"Extract and refill cavity wall insulation with {material['description']}"
else:
description = self._make_description(material)
# updated the new u-value with the best possible our installers have
new_u_value = max(0.31, new_u_value)
recommendations.append(
{
"phase": phase,
@ -237,7 +250,7 @@ class WallRecommendations(Definitions):
)
],
"type": "cavity_wall_insulation",
"description": self._make_description(material),
"description": description,
"starting_u_value": u_value,
"new_u_value": new_u_value,
"sap_points": None,