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added off-gas property recommendations

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Khalim Conn-Kowlessar 2024-04-03 19:17:27 +01:00
parent f2cec8de11
commit 519dc6cfcb
5 changed files with 247 additions and 10 deletions
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1
backend/app/plan/router.py
View file

@ -389,6 +389,7 @@ async def trigger_plan(body: PlanTriggerRequest):
# Commit final changes
session.commit()
except IntegrityError:
logger.error("Database integrity error occurred", exc_info=True)
session.rollback()

42
etl/customers/gla_croydon_demo/asset_list.py
View file

@ -4,6 +4,23 @@ from utils.s3 import save_csv_to_s3
USER_ID = 8
PORTFOLIO_ID = 67
archetype_1_uprns = [100020604138, 200001188299, 100020578756, 200001187196, 200001192253, 100020581792, 200001188304,
100020625813, 100020618060, 100020585305, 100020617489, 100020615039, 100020618076, 100020588913,
200001187197, 100020671205, 100020576940, 100020619814, 100020576472, 100020618083]
archetype_2_uprns = [100020698027, 10001007455, 100020653785, 10090383198, 100020665632, 100020620659, 100020615603,
100020609610, 100020625597, 100020665656, 100020665640, 100020587905, 100020665630, 100020624351,
100020625451, 100020624348, 100020666735, 100020653786, 100020576458, 100020657902, 100020624350,
100020637405, 100020666734, 100020616325, 100020666716, 100020653783, 100020665645, 100020642337,
100020665638, 100022904981, 100020688226, 100020630285, 100020626800, 100020665634, 100022907528,
100020665652, 100020624347, 100020666721, 100020585002, 10014055968, 10001008257, 100020621438,
100020576459, 100020665643, 100020665654, 100022917303]
archetype_3_uprns = [100020577523, 100020616446, 100020605342, 100020594652, 100020585394, 100020601138, 100020597485,
100020614883, 100020633162, 100020697787, 200001185785, 100020646842, 100020581449, 100020595611,
100020641814, 100020575611, 100020652986, 100020654671, 100020647336, 100020610518, 100020607980,
100020692380, 100020581690]
archetype_4_uprns = [100020650603, 100020582907, 100020605116, 100020650607, 100020589325, 100020655500, 100020642537,
200001187539, 100020631683, 100020610165, 100020596436, 100020598277, 100020660228]
def app():
"""
@ -84,14 +101,15 @@ def app():
archetype_2_sample_asset_list["ARCHETYPE"] = "Archetype 2"
# Archetype 3: defined below:
# 1) EPC F
# 1) EPC E or below
# 2) Solid brick wall
# 3) House
# 4) Pitched roof with no insulation
# Just 7 properties (more expensive to retrofit)
archetype_3_sample = epc_data[
epc_data["PROPERTY_TYPE"].isin(["House"]) &
(epc_data["CURRENT_ENERGY_RATING"].isin(["F", "G"])) &
(epc_data["CURRENT_ENERGY_RATING"].isin(["E", "F", "G"])) &
epc_data["WALLS_DESCRIPTION"].isin(["Solid brick, as built, no insulation (assumed)"]) &
epc_data["ROOF_DESCRIPTION"].isin(
[
"Pitched, no insulation",
@ -119,7 +137,6 @@ def app():
archetype_4_sample_asset_list = archetype_4_sample[["UPRN", "ADDRESS1", "POSTCODE"]].copy()
archetype_4_sample_asset_list["ARCHETYPE"] = "Archetype 4"
# 104 total properties
asset_list = pd.concat(
[
archetype_1_sample_asset_list,
@ -152,6 +169,25 @@ def app():
]
)
]
# We have slightly too many properties, so we take a random sample of each archetype
# achetype_1_size = 20
# achetype_2_size = 46
# achetype_3_size = 23
# achetype_4_size = 13
# archetype_1_uprns = asset_list[asset_list["archetype"] == "Archetype 1"]["uprn"].sample(
# int(achetype_1_size)
# ).tolist()
# archetype_2_uprns = asset_list[asset_list["archetype"] == "Archetype 2"]["uprn"].sample(
# int(achetype_2_size)
# ).tolist()
# archetype_3_uprns = asset_list[asset_list["archetype"] == "Archetype 3"]["uprn"].sample(
# int(achetype_3_size)
# ).tolist()
# archetype_4_uprns = asset_list[asset_list["archetype"] == "Archetype 4"]["uprn"].sample(
# int(achetype_4_size)
# ).tolist()
uprns_to_keep = archetype_1_uprns + archetype_2_uprns + archetype_3_uprns + archetype_4_uprns
asset_list = asset_list[asset_list["uprn"].isin(uprns_to_keep)]
filename = f"{USER_ID}/{PORTFOLIO_ID}/inputs.csv"
save_csv_to_s3(

200
etl/customers/gla_croydon_demo/slides.py
View file

@ -27,8 +27,24 @@ SAP_TARGET_1 = 69
SAP_TARGET_2 = 100
CUSTOMER_KEY = "gla-demo"
# Sample UPRNS
archetype_1_sample = ['100020618076', '100020619814', '100020581792', '100020671205', '100020585305', '100020606853',
'100020625813', '100020618042', '200001188304', '200001187196', '100020603026', '100020604138',
'100020615039', '200001188299', '100020618060', '200001192253']
def app():
archetype_2_sample = ['100020616325', '100020665634', '100020665654', '100020665638', '100020587936', '100020587905',
'100020665645', '100020625597', '100022907528', '100020665630', '100020624348', '10001008257',
'100020666735', '100020698027', '100020624351', '100020665656', '100020666716', '100020665632',
'100020666715', '100020645639', '200001191309', '100020625451', '100020624347', '100020665658',
'100020585002', '100022917303', '100020665650', '100020667737', '100020620659', '100022904981',
'100020642337', '100020657902', '100020615603', '100020626800', '100020665647', '100020665643']
archetype_3_sample = ['100020607980', '200001193193', '100020581690', '100020665611']
archetype_4_sample = ['100020631683', '100020607667', '100020660228', '100020605116', '200001187539', '100020582907',
'100020610165', '100020650607', '100020655500', '100020598277', '100020642537']
def scenario_1():
# Connect to database
session = sessionmaker(bind=db_engine)()
@ -36,9 +52,7 @@ def app():
# Get the data we need
########################################################################
# TODO: Update to portfolio desired
# portfolio_id = PORTFOLIO_ID_1
portfolio_id = PORTFOLIO_ID_2
portfolio_id = PORTFOLIO_ID_1
# Get the asset list
asset_list = read_csv_from_s3(
@ -157,3 +171,181 @@ def app():
# Overview
########################
overview_totals = recommendations_summary.sum()
def make_sample():
# sample_proportion = 67 / 102
# Get the asset list
asset_list = read_csv_from_s3(
"retrofit-plan-inputs-dev", f"{USER_ID}/67/inputs.csv"
)
asset_list = pd.DataFrame(asset_list)
# From the asset list, we deduce how many properties we need
archetype_1_sample_size = 16
archetype_2_sample_size = 36
archetype_3_sample_size = 4
archetype_4_sample_size = 11
# We take the sample and we'll keep the uprns static
archetype_1_sample = asset_list[
asset_list["archetype"] == "Archetype 1"
].sample(archetype_1_sample_size)["uprn"].to_list()
archetype_2_sample = asset_list[
asset_list["archetype"] == "Archetype 2"
].sample(archetype_2_sample_size)["uprn"].to_list()
archetype_3_sample = asset_list[
asset_list["archetype"] == "Archetype 3"
].sample(archetype_3_sample_size)["uprn"].to_list()
archetype_4_sample = asset_list[
asset_list["archetype"] == "Archetype 4"
].sample(archetype_4_sample_size)["uprn"].to_list()
def scenario_2():
# Connect to database
session = sessionmaker(bind=db_engine)()
########################################################################
# Get the data we need
########################################################################
portfolio_id = PORTFOLIO_ID_2
# Get the asset list
asset_list = read_csv_from_s3(
"retrofit-plan-inputs-dev", f"{USER_ID}/67/inputs.csv"
)
asset_list = pd.DataFrame(asset_list)
sample_uprns = archetype_1_sample + archetype_2_sample + archetype_3_sample + archetype_4_sample
# Filter on sample uprns
asset_list = asset_list[asset_list["uprn"].astype(str).isin(sample_uprns)]
# Get the properties for the portfolio
properties = get_properties_with_default_recommendations(session, portfolio_id)
properties_df = pd.DataFrame(properties)
properties_df = properties_df[properties_df["uprn"].astype(str).isin(sample_uprns)]
# We now pull the data for the property details
property_details = get_property_details_by_portfolio_id(session, portfolio_id)
property_details_df = pd.DataFrame(property_details)
property_details_df = property_details_df[property_details_df["property_id"].isin(properties_df["id"].values)]
# We estimate bills based on the adjusted_energy_consumption
property_details_df["energy_bill"] = property_details_df["adjusted_energy_consumption"].apply(
lambda x: AnnualBillSavings.calculate_annual_bill(x)
)
# Merge on uprn
property_details_df = property_details_df.merge(
properties_df[["uprn", "id"]].rename(columns={"id": "property_id"}),
on="property_id"
)
plans = get_plan_by_portfolio_id(session, portfolio_id)
plans_df = pd.DataFrame(plans)
# Unnest the recommendations. Each recommendation is a list of dictionaries
recommendations_exploded = properties_df["recommendations"].explode().tolist()
recommendations_df = pd.DataFrame([r for r in recommendations_exploded if not pd.isnull(r)])
# Add uprn on
recommendations_df = recommendations_df.merge(
properties_df[["uprn", "id"]].rename(columns={"id": "property_id"}),
how="left",
on="property_id"
)
recommendations_summary = create_recommendations_summary(
recommendations_df,
properties_df,
property_details_df,
SAP_TARGET_1
)
# Calculate % changes of energ, co2 and abs
recommendations_summary["carbon_percent_change"] = (
recommendations_summary["total_carbon"] / recommendations_summary["current_co2"]
)
recommendations_summary["energy_percent_change"] = (
recommendations_summary["adjusted_heat_demand"] / recommendations_summary["current_energy"]
)
recommendations_summary["bills_percent_change"] = (
recommendations_summary["total_bill_savings"] / recommendations_summary["current_energy_bill"]
)
########################
# Overview
########################
overview_totals = recommendations_summary.sum()
overview_means = recommendations_summary.mean()
########################
# Measures
########################
measures_count = recommendations_df.groupby("type")["id"].count().reset_index()
z = recommendations_df[recommendations_df["uprn"].astype(str).isin(archetype_3_sample)]
recommendations_df[recommendations_df["uprn"].astype(str).isin(archetype_3_sample)]["type"].value_counts()
# Summary information by each archetype
########################
# Archetype 1
########################
archetype_1 = asset_list[asset_list["archetype"] == "Archetype 1"]
recommendations_arch_1_summary = recommendations_summary[
recommendations_summary["uprn"].astype(str).isin(archetype_1["uprn"].values)
]
# Take the mean, median and maximum of each value
arch_1_recommendation_min = recommendations_arch_1_summary.min()
arch_1_recommendation_max = recommendations_arch_1_summary.max()
arch_1_recommendation_means = recommendations_arch_1_summary.mean()
########################
# Archetype 2
########################
archetype_2 = asset_list[asset_list["archetype"] == "Archetype 2"]
recommendations_arch_2_summary = recommendations_summary[
recommendations_summary["uprn"].astype(str).isin(archetype_2["uprn"].values)
]
# Take the mean, median and maximum of each value
arch_2_recommendation_min = recommendations_arch_2_summary.min()
arch_2_recommendation_max = recommendations_arch_2_summary.max()
arch_2_recommendation_means = recommendations_arch_2_summary.mean().round(2)
########################
# Archetype 3
########################
archetype_3 = asset_list[asset_list["archetype"] == "Archetype 3"]
recommendations_arch_3_summary = recommendations_summary[
recommendations_summary["uprn"].astype(str).isin(archetype_3["uprn"].values)
]
# Take the mean, median and maximum of each value
arch_3_recommendation_min = recommendations_arch_3_summary.min()
arch_3_recommendation_max = recommendations_arch_3_summary.max()
arch_3_recommendation_means = recommendations_arch_3_summary.mean()
########################
# Archetype 4
########################
archetype_4 = asset_list[asset_list["archetype"] == "Archetype 4"]
recommendations_arch_4_summary = recommendations_summary[
recommendations_summary["uprn"].astype(str).isin(archetype_4["uprn"].values)
]
# Take the mean, median and maximum of each value
arch_4_recommendation_min = recommendations_arch_4_summary.min()
arch_4_recommendation_max = recommendations_arch_4_summary.max()
arch_4_recommendation_means = recommendations_arch_4_summary.mean()
property_details_df[
property_details_df["uprn"].astype(str).isin(archetype_4["uprn"].values)
]["total_floor_area"].mean()

2
recommendations/HeatingControlRecommender.py
View file

@ -215,7 +215,7 @@ class HeatingControlRecommender:
{
"type": "heating_control",
"parts": [],
"description": "Upgrade heating controls to Time and Temperature Zone Controls",
"description": "Upgrade heating controls to Smart Thermostats, room sensors and smart radiator valves",
**self.costs.time_and_temperature_zone_control(
number_heated_rooms=int(self.property.data["number-heated-rooms"])
),

12
recommendations/HeatingRecommender.py
View file

@ -19,9 +19,17 @@ class HeatingRecommender:
self.recommendations = []
# This first iteration of the recommender will provide very basic recommendation
# We recommend heating controls based on the main heating system
if self.property.main_heating["clean_description"] in [
has_electric_heating_description = self.property.main_heating["clean_description"] in [
"Room heaters, electric", "Electric storage heaters", "Electric storage heaters, radiators"
]:
]
no_heating_no_mains = (
self.property.main_heating["clean_description"] in ["No system present, electric heaters assumed"] and
not self.property.data["mains-gas-flag"]
)
if has_electric_heating_description or no_heating_no_mains:
# Recommend high heat retention storage heaters
self.recommend_electric_storage_heaters(phase=phase, system_change=True, heating_controls_only=False)
return