junte/Model
1
0
Fork 0
mirror of https://github.com/Hestia-Homes/Model.git synced 2026-06-08 11:17:27 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

exporting data for newhaven

Browse source
This commit is contained in:
Khalim Conn-Kowlessar 2024-10-21 12:28:25 +01:00
parent 681a449187
commit 6a83fe7d9b
2 changed files with 48 additions and 27 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

15
backend/ml_models/Valuation.py
View file

@ -204,12 +204,12 @@ class PropertyValuation:
@classmethod
def estimate(cls, property_instance, target_epc):
value = (
current_value = (
property_instance.valuation if property_instance.valuation else
cls.UPRN_VALUE_LOOKUP.get(property_instance.uprn)
)
if not value:
if not current_value:
return {
"current_value": 0,
"lower_bound_increased_value": 0,
@ -239,12 +239,13 @@ class PropertyValuation:
max_increase = max(all_increases)
min_increase = min(all_increases)
avg_increase = np.mean(all_increases)
return {
"current_value": value,
"lower_bound_increased_value": value * (1 + min_increase),
"upper_bound_increased_value": value * (1 + max_increase),
"average_increased_value": value * (1 + avg_increase),
"average_increase": value * (1 + avg_increase) - value
"current_value": current_value,
"lower_bound_increased_value": current_value * (1 + min_increase),
"upper_bound_increased_value": current_value * (1 + max_increase),
"average_increased_value": current_value * (1 + avg_increase),
"average_increase": current_value * (1 + avg_increase) - current_value
}

60
etl/customers/newhaven/slides.py
View file

@ -418,7 +418,7 @@ def slides():
pd.set_option('display.max_colwidth', None)
def app():
def lewes_outputs():
"""
preparing of this data for the following 2 needs:
1) dataset to share with Nextgen heating
@ -525,13 +525,13 @@ def app():
recommendations_df["type"] == "heating",
np.where(
recommendations_df["description"].str.contains("air source heat pump"),
"air_source_heat_pump",
"Air Source Heat Pump",
np.where(
recommendations_df["description"].str.contains("high heat retention"),
"high_heat_retention_storage_heaters",
"High Heat Retention Storage",
np.where(
recommendations_df["description"].str.contains("condensing boiler"),
"boiler_upgrade",
"Boiler Upgrade",
recommendations_df["type"]
)
)
@ -552,7 +552,6 @@ def app():
'room_roof_insulation': 'Room Roof Insulation',
'low_energy_lighting': 'Low Energy Lighting',
'external_wall_insulation': 'External Wall Insulation',
'heating': 'Heating',
'solar_pv': 'Solar PV',
'heating_control': 'Heating Control',
'solid_floor_insulation': 'Solid Floor Insulation',
@ -634,7 +633,7 @@ def app():
property_scenario_impact = pd.concat(property_scenario_impact)
# property_scenario_impact = property_scenario_impact.drop(columns=["property_id", "Estimated Kwh Savings"])
for v in rename_dict.values():
for v in list(rename_dict.values()) + ["Air Source Heat Pump", "High Heat Retention Storage", "Boiler Upgrade"]:
# Fill NaNs with False
property_scenario_impact[v] = property_scenario_impact[v].fillna(False)
@ -731,45 +730,66 @@ def app():
scenario_names, how="left", on="scenario_id"
)
property_scenario_impact = property_scenario_impact.sort_values(
["postcode", "uprn", "scenario_id"], ascending=True
)
lewes_data = next_gen_dataset.merge(
property_scenario_impact, how="left", on="uprn"
)
lewes_data = lewes_data.sort_values(
["postcode", "uprn", "scenario_id"], ascending=True
)
# Rearrange, rename columns and drop what we don't need
# TODO - remap the heating type
lewes_data = lewes_data[
[
'uprn', 'address', 'postcode', 'property_type', 'built_form', 'estimated_heating_hotwater_kwh',
'uprn', 'address', 'postcode', 'property_type', 'built_form',
# 'estimated_heating_hotwater_kwh',
'primary_fuel_type', 'gross_floor_area', 'floor_height', 'number_of_floors', 'ashp_suitable',
'ashp_size_kw',
'ashp_cost', 'solar_suitable', 'solar_size_kwp', 'solar_cost', 'estimated_heating_hotwater_kwh_scaled',
'ashp_cost', 'solar_suitable', 'solar_size_kwp', 'solar_cost',
'scenario',
'estimated_heating_hotwater_kwh_scaled',
'post_scenario_heating_hotwater_kwh_scaled',
# 'property_id', - dropped
'current_energy_demand_heating_hotwater', 'Estimated Heating Demand kWh Savings',
'Estimated Lighting kWh Savings', 'Estimated Solar kWh Savings', 'estimated_cost',
# 'current_energy_demand_heating_hotwater',
'Estimated Heating Demand kWh Savings',
'Estimated Lighting kWh Savings',
'Estimated Solar kWh Savings',
'estimated_cost',
'post_scenario_heating_hotwater_kwh', 'Cavity Wall Insulation', 'Cylinder Thermostat',
'Flat Roof Insulation',
'Hot Water Tank Insulation', 'Loft Insulation', 'Mechanical Ventilation', 'Room Roof Insulation',
# 'scenario_id', - dropped
'Low Energy Lighting', 'Secondary Heating', 'Windows Glazing', 'External Wall Insulation',
'Heating',
'Heating Control',
'Solar PV',
'Air Source Heat Pump', 'Boiler Upgrade', 'High Heat Retention Storage',
'Internal Wall Insulation',
'Solid Floor Insulation',
'Suspended Floor Insulation',
'post_scenario_heating_hotwater_kwh_scaled',
'scenario'
]
]
].rename(
columns={
"primary_fuel_type": "Primary Fuel Type",
"gross_floor_area": "Gross Floor Area",
"floor_height": "Floor Height",
"number_of_floors": "Number of Floors",
"ashp_suitable": "Is an ASHP Suitable?",
"ashp_size_kw": "ASHP Size (kW)",
"ashp_cost": "ASHP Cost",
"solar_suitable": "Is Solar PV Suitable?",
"solar_size_kwp": "Solar PV Size (kWp)",
"solar_cost": "Solar PV Cost",
# "estimated_heating_hotwater_kwh": "Estimated Heating & Hot Water kwh",
"estimated_heating_hotwater_kwh_scaled": "Estimated Heating & Hot Water kwh",
"post_scenario_heating_hotwater_kwh_scaled": "Post Scenario Heating & Hot Water kwh",
"estimated_cost": "Estimated Cost of Scenario"
}
)
# We save this dataset, which will be shared with Lewes Council
lewes_data.to_csv(
"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Newhaven/outputs/property data.csv", index=False
"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Newhaven/outputs/Lewes property data.csv", index=False
)
df_pivot = property_scenario_impact.pivot_table(index='uprn', columns='scenario',