mirror of
https://github.com/Hestia-Homes/Model.git
synced 2026-06-08 11:17:27 +00:00
cleaning setting of kwh and energy bills
This commit is contained in:
Khalim Conn-Kowlessar
parent
6ec7995ac3
commit
2cb5308711
5 changed files with 236 additions and 223 deletions
|
|
@ -30,6 +30,7 @@ from backend.app.plan.utils import get_cleaned
|
|||
from backend.app.utils import epc_to_sap_lower_bound, sap_to_epc
|
||||
|
||||
from backend.ml_models.api import ModelApi
|
||||
from backend.ml_models.AnnualBillSavings import AnnualBillSavings
|
||||
from backend.Property import Property
|
||||
from backend.apis.GoogleSolarApi import GoogleSolarApi
|
||||
|
||||
|
|
@ -722,228 +723,12 @@ async def trigger_plan(body: PlanTriggerRequest):
|
|||
property_recommendations = recommendations[property_id]
|
||||
property_instance = [p for p in input_properties if p.id == property_id][0]
|
||||
|
||||
kwh_impact_table = kwh_simulation_predictions["heating_kwh_predictions"][
|
||||
kwh_simulation_predictions["heating_kwh_predictions"]["property_id"] == str(property_id)
|
||||
].merge(
|
||||
kwh_simulation_predictions["hotwater_kwh_predictions"].drop(
|
||||
columns=["property_id", "recommendation_id", "phase"]
|
||||
),
|
||||
how="inner",
|
||||
on="id",
|
||||
suffixes=("_heating", "_hotwater")
|
||||
).reset_index(drop=True)
|
||||
|
||||
# We adjust this table with the kwh estimates for low energy lighting kwh values, and solar kwh estimates
|
||||
led_recommendation = pd.DataFrame([
|
||||
{
|
||||
"phase": r["phase"],
|
||||
"recommendation_id": r["recommendation_id"],
|
||||
"lighting_kwh_savings": r["kwh_savings"] * GoogleSolarApi.SOLAR_CONSUMPTION_PROPORTION,
|
||||
} for recs in property_recommendations for r in recs if r["type"] == "low_energy_lighting"
|
||||
], columns=["phase", "recommendation_id", "lighting_kwh_savings"])
|
||||
solar_recommendations = pd.DataFrame([
|
||||
{
|
||||
"phase": r["phase"],
|
||||
"recommendation_id": r["recommendation_id"],
|
||||
"solar_kwh_savings": r["initial_ac_kwh_per_year"] * GoogleSolarApi.SOLAR_CONSUMPTION_PROPORTION,
|
||||
} for recs in property_recommendations for r in recs if r["type"] == "solar_pv"
|
||||
], columns=["phase", "recommendation_id", "solar_kwh_savings"])
|
||||
|
||||
# merge them on
|
||||
kwh_impact_table = kwh_impact_table.merge(
|
||||
led_recommendation, how="left", on=["phase", "recommendation_id"]
|
||||
).merge(
|
||||
solar_recommendations, how="left", on=["phase", "recommendation_id"]
|
||||
)
|
||||
|
||||
property_kwh = property_instance.energy_consumption_estimates["unadjusted"]
|
||||
|
||||
starting_dummy_id_value = -9999
|
||||
kwh_impact_table = pd.concat(
|
||||
[
|
||||
pd.DataFrame(
|
||||
[
|
||||
{
|
||||
"id": starting_dummy_id_value,
|
||||
"phase": starting_dummy_id_value,
|
||||
"recommendation_id": starting_dummy_id_value,
|
||||
"predictions_heating": property_kwh["heating"],
|
||||
"predictions_hotwater": property_kwh["hot_water"],
|
||||
}
|
||||
]
|
||||
),
|
||||
kwh_impact_table
|
||||
]
|
||||
).sort_values(["phase", "recommendation_id"], ascending=True).reset_index(drop=True)
|
||||
|
||||
for i in range(0, len(kwh_impact_table)):
|
||||
current_phase = kwh_impact_table.loc[i, 'phase']
|
||||
previous_phase_id = (current_phase - 1) if (current_phase > 0) else -9999
|
||||
previous_phase = kwh_impact_table[kwh_impact_table['phase'] == previous_phase_id]
|
||||
|
||||
if not previous_phase.empty:
|
||||
for col in ["predictions_heating", "predictions_hotwater"]:
|
||||
if kwh_impact_table.loc[i, col] > previous_phase[col].max():
|
||||
kwh_impact_table.loc[i, col] = previous_phase[col].max()
|
||||
|
||||
from backend.ml_models.AnnualBillSavings import AnnualBillSavings
|
||||
# We adjust the predictions with the UCL model
|
||||
for k in ["heating", "hotwater"]:
|
||||
kwh_impact_table[f"adjusted_{k}"] = kwh_impact_table[f"predictions_{k}"].apply(
|
||||
lambda x: AnnualBillSavings.adjust_energy_to_metered(
|
||||
epc_energy=x, current_epc_rating=property_instance.data["current-energy-rating"]
|
||||
)
|
||||
)
|
||||
|
||||
ASHP_COP = 3
|
||||
descriptions_to_fuel_types = {
|
||||
"Air source heat pump, radiators, electric": {"fuel": "Electricity", "cop": ASHP_COP},
|
||||
"Boiler and radiators, mains gas": {"fuel": 'Natural Gas', "cop": 0.9},
|
||||
'Electric storage heaters': {"fuel": 'Electricity', "cop": 1},
|
||||
"Electric immersion, off-peak": {"fuel": 'Electricity', "cop": 1},
|
||||
"Electric storage heaters, radiators": {"fuel": 'Electricity', "cop": 1},
|
||||
"Room heaters, electric": {"fuel": 'Electricity', "cop": 1},
|
||||
"Electric immersion, standard tariff": {"fuel": 'Electricity', "cop": 1},
|
||||
"Portable electric heaters assumed for most rooms": {"fuel": 'Electricity', "cop": 1},
|
||||
}
|
||||
|
||||
def map_descriptions_to_fuel(heating_description, hotwater_description):
|
||||
mapped = descriptions_to_fuel_types[heating_description]
|
||||
heating_fuel = mapped["fuel"]
|
||||
|
||||
if hotwater_description == "From main system":
|
||||
return {
|
||||
"heating_fuel_type": heating_fuel, "hotwater_fuel_type": heating_fuel,
|
||||
"heating_cop": mapped["cop"], "hotwater_cop": mapped["cop"]
|
||||
}
|
||||
|
||||
mapped_hotwater = descriptions_to_fuel_types[hotwater_description]
|
||||
|
||||
return {
|
||||
"heating_fuel_type": heating_fuel, "hotwater_fuel_type": mapped_hotwater["fuel"],
|
||||
"heating_cop": mapped["cop"], "hotwater_cop": mapped_hotwater["cop"]
|
||||
}
|
||||
|
||||
# For heating system recommendations, this could result in a fuel type change so we reflect that
|
||||
fuel_mapping = pd.DataFrame([
|
||||
{
|
||||
"id": epc["id"],
|
||||
**map_descriptions_to_fuel(epc["mainheat-description"], epc["hotwater-description"])
|
||||
} for epc in property_instance.updated_simulation_epcs
|
||||
])
|
||||
|
||||
for epc in property_instance.updated_simulation_epcs:
|
||||
map_descriptions_to_fuel(epc["mainheat-description"], epc["hotwater-description"])
|
||||
|
||||
fuel_mapping = pd.concat(
|
||||
[
|
||||
pd.DataFrame(
|
||||
[
|
||||
{
|
||||
"id": starting_dummy_id_value,
|
||||
**map_descriptions_to_fuel(
|
||||
property_instance.data["mainheat-description"],
|
||||
property_instance.data["hotwater-description"]
|
||||
)
|
||||
}
|
||||
]
|
||||
),
|
||||
fuel_mapping
|
||||
]
|
||||
)
|
||||
|
||||
kwh_impact_table = kwh_impact_table.merge(
|
||||
fuel_mapping, how="left", on="id"
|
||||
).sort_values(["phase", "recommendation_id"], ascending=True).reset_index(drop=True)
|
||||
|
||||
kwh_impact_table["heating_fuel_type"] = np.where(
|
||||
kwh_impact_table["id"] == starting_dummy_id_value,
|
||||
property_instance.heating_energy_source,
|
||||
kwh_impact_table["heating_fuel_type"]
|
||||
)
|
||||
|
||||
kwh_impact_table["hotwater_fuel_type"] = np.where(
|
||||
kwh_impact_table["id"] == starting_dummy_id_value,
|
||||
property_instance.hot_water_energy_source,
|
||||
kwh_impact_table["hotwater_fuel_type"]
|
||||
)
|
||||
|
||||
def calculate_recommendation_fuel_cost(kwh, fuel, cop):
|
||||
if fuel == "Electricity":
|
||||
return (kwh / cop) * AnnualBillSavings.ELECTRICITY_PRICE_CAP
|
||||
|
||||
if fuel == "Natural Gas":
|
||||
return (kwh / cop) * AnnualBillSavings.GAS_PRICE_CAP
|
||||
|
||||
# We now calculate the fuel cost
|
||||
for k in ["heating", "hotwater"]:
|
||||
kwh_impact_table[f"{k}_cost"] = kwh_impact_table.apply(
|
||||
lambda x: calculate_recommendation_fuel_cost(
|
||||
x[f"adjusted_{k}"], x[f"{k}_fuel_type"], x[f"{k}_cop"]
|
||||
), axis=1
|
||||
)
|
||||
|
||||
# TODO: The impact of remapping EPC is huge!
|
||||
|
||||
# We now deduce if any of the recommendations result in a change of fuel type
|
||||
for recs in property_recommendations:
|
||||
for rec in recs:
|
||||
if rec["type"] == "mechanical_ventilation":
|
||||
continue
|
||||
|
||||
rec_impact = kwh_impact_table[kwh_impact_table["recommendation_id"] == rec["recommendation_id"]]
|
||||
prevous_phase_id = (rec["phase"] - 1) if (rec["phase"] > 0) else starting_dummy_id_value
|
||||
previous_phase_impact = kwh_impact_table[kwh_impact_table["phase"] == prevous_phase_id]
|
||||
|
||||
if rec["type"] == "solar_pv":
|
||||
rec["kwh_savings"] = rec_impact["solar_kwh_savings"].values[0]
|
||||
rec["energy_cost_savings"] = (
|
||||
rec_impact["solar_kwh_savings"].values[0] * AnnualBillSavings.ELECTRICITY_PRICE_CAP
|
||||
)
|
||||
continue
|
||||
|
||||
heating_kwh_savings = (
|
||||
previous_phase_impact["adjusted_heating"].mean() - rec_impact["adjusted_heating"].values[0]
|
||||
)
|
||||
heating_cost_savings = (
|
||||
previous_phase_impact["heating_cost"].mean() - rec_impact["heating_cost"].values[0]
|
||||
)
|
||||
|
||||
hotwater_kwh_savings = (
|
||||
previous_phase_impact["adjusted_hotwater"].mean() - rec_impact["adjusted_hotwater"].values[0]
|
||||
)
|
||||
hotwater_host = (
|
||||
previous_phase_impact["hotwater_cost"].mean() - rec_impact["hotwater_cost"].values[0]
|
||||
)
|
||||
|
||||
total_kwh_savings = heating_kwh_savings + hotwater_kwh_savings
|
||||
energy_cost_savings = heating_cost_savings + hotwater_host
|
||||
|
||||
if rec["type"] == "lighting":
|
||||
# In this case, we should probably just SKIP but check when we have one!
|
||||
raise Exception("Implement me 3")
|
||||
|
||||
rec["kwh_savings"] = total_kwh_savings
|
||||
rec["energy_cost_savings"] = energy_cost_savings
|
||||
|
||||
# Finally, we set the current energy bill
|
||||
starting_figures = kwh_impact_table[kwh_impact_table["id"] == starting_dummy_id_value].squeeze()
|
||||
gas_standing_charge = 0
|
||||
if (
|
||||
(starting_figures["heating_fuel_type"] == "Natural Gas") or
|
||||
(starting_figures["hotwater_fuel_type"] == "Natural Gas")
|
||||
):
|
||||
gas_standing_charge = AnnualBillSavings.DAILY_STANDARD_CHARGE_GAS * 365
|
||||
|
||||
electricity_standing_charge = AnnualBillSavings.DAILY_STANDARD_CHARGE_ELECTRICITY * 365
|
||||
|
||||
property_instance.current_energy_bill = (
|
||||
starting_figures["heating_cost"].values[0] +
|
||||
starting_figures["hotwater_cost"].values[0] +
|
||||
property_instance.energy_cost_estimates["unadjusted"]["lighting"] +
|
||||
property_instance.energy_cost_estimates["unadjusted"]["appliances"] +
|
||||
gas_standing_charge + electricity_standing_charge
|
||||
property_current_energy_bill = Recommendations.calculate_recommendation_tenant_savings(
|
||||
property_instance=property_instance,
|
||||
kwh_simulation_predictions=kwh_simulation_predictions,
|
||||
property_recommendations=property_recommendations
|
||||
)
|
||||
property_instance.current_energy_bill = property_current_energy_bill
|
||||
|
||||
# Insert the predictions into the recommendations and run the optimiser
|
||||
# TODO: If a recommendation has a negative impact on SAP, we should remove it - this seems to have become a
|
||||
|
|
@ -1284,7 +1069,7 @@ async def build_mds(body: MdsRequest):
|
|||
recommendations = {}
|
||||
|
||||
for p in tqdm(input_properties):
|
||||
p.get_components(cleaned, photo_supply_lookup, floor_area_decile_thresholds)
|
||||
p.set_features(cleaned, photo_supply_lookup, floor_area_decile_thresholds)
|
||||
|
||||
mds = Mds(property_instance=p, materials=materials, optimise_measures=optimise_measures)
|
||||
mds_recommendations, property_representative_recommendations, errors = mds.build()
|
||||
|
|
|
|||
|
|
@ -199,3 +199,11 @@ class AnnualBillSavings:
|
|||
return current_epc_rating
|
||||
|
||||
return cls.EPC_BANDS[expected_index - 1]
|
||||
|
||||
@classmethod
|
||||
def calculate_recommendation_fuel_cost(cls, kwh, fuel, cop):
|
||||
if fuel == "Electricity":
|
||||
return (kwh / cop) * cls.ELECTRICITY_PRICE_CAP
|
||||
|
||||
if fuel == "Natural Gas":
|
||||
return (kwh / cop) * cls.GAS_PRICE_CAP
|
||||
|
|
|
|||
|
|
@ -134,7 +134,7 @@ def app():
|
|||
for i, directory in tqdm(enumerate(epc_directories), total=len(epc_directories)):
|
||||
try:
|
||||
# Skip the first 50
|
||||
if i < 40:
|
||||
if i < 200:
|
||||
continue
|
||||
|
||||
data = pd.read_csv(directory / "certificates.csv", low_memory=False)
|
||||
|
|
|
|||
|
|
@ -17,6 +17,8 @@ def app():
|
|||
|
||||
cleaned = msgpack.unpackb(cleaned, raw=False)
|
||||
|
||||
# If there is any problematic data, it could be:
|
||||
# s3://retrofit-datalake-dev/energy_consumption_data/2024-08-10 18:48:06.866647.pkl
|
||||
kwh_data_client = KwhData(bucket="retrofit-datalake-dev")
|
||||
kwh_data_client.combine()
|
||||
kwh_data_client.transform(data=kwh_data_client.data, cleaned=cleaned, save=True)
|
||||
|
|
|
|||
|
|
@ -17,6 +17,19 @@ from recommendations.SecondaryHeating import SecondaryHeating
|
|||
from backend.ml_models.AnnualBillSavings import AnnualBillSavings
|
||||
from backend.apis.GoogleSolarApi import GoogleSolarApi
|
||||
|
||||
ASHP_COP = 3
|
||||
DESCRIPTIONS_TO_FUEL_TYPES = {
|
||||
"Air source heat pump, radiators, electric": {"fuel": "Electricity", "cop": ASHP_COP},
|
||||
"Boiler and radiators, mains gas": {"fuel": 'Natural Gas', "cop": 0.9},
|
||||
'Electric storage heaters': {"fuel": 'Electricity', "cop": 1},
|
||||
"Electric immersion, off-peak": {"fuel": 'Electricity', "cop": 1},
|
||||
"Electric storage heaters, radiators": {"fuel": 'Electricity', "cop": 1},
|
||||
"Room heaters, electric": {"fuel": 'Electricity', "cop": 1},
|
||||
"Electric immersion, standard tariff": {"fuel": 'Electricity', "cop": 1},
|
||||
"Portable electric heaters assumed for most rooms": {"fuel": 'Electricity', "cop": 1},
|
||||
}
|
||||
STARTING_DUMMY_ID_VALUE = -9999
|
||||
|
||||
|
||||
class Recommendations:
|
||||
"""
|
||||
|
|
@ -497,3 +510,208 @@ class Recommendations:
|
|||
)
|
||||
|
||||
return property_recommendations, impact_summary
|
||||
|
||||
@staticmethod
|
||||
def map_descriptions_to_fuel(heating_description, hotwater_description):
|
||||
mapped = DESCRIPTIONS_TO_FUEL_TYPES[heating_description]
|
||||
heating_fuel = mapped["fuel"]
|
||||
|
||||
if hotwater_description == "From main system":
|
||||
return {
|
||||
"heating_fuel_type": heating_fuel, "hotwater_fuel_type": heating_fuel,
|
||||
"heating_cop": mapped["cop"], "hotwater_cop": mapped["cop"]
|
||||
}
|
||||
|
||||
mapped_hotwater = DESCRIPTIONS_TO_FUEL_TYPES[hotwater_description]
|
||||
|
||||
return {
|
||||
"heating_fuel_type": heating_fuel, "hotwater_fuel_type": mapped_hotwater["fuel"],
|
||||
"heating_cop": mapped["cop"], "hotwater_cop": mapped_hotwater["cop"]
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def calculate_recommendation_tenant_savings(
|
||||
cls, property_instance, kwh_simulation_predictions, property_recommendations
|
||||
):
|
||||
"""
|
||||
This method inserts the kwh savings and the bill savings that the customer will make from the recommendations
|
||||
based on the predictions from the ML model
|
||||
:param property_instance: Instance of the Property class, for the home associated to property_id
|
||||
:param kwh_simulation_predictions: dictionary of predictions from the model apis
|
||||
:param property_recommendations: dictionary of recommendations for the property
|
||||
:return:
|
||||
"""
|
||||
|
||||
kwh_impact_table = kwh_simulation_predictions["heating_kwh_predictions"][
|
||||
kwh_simulation_predictions["heating_kwh_predictions"]["property_id"] == str(property_instance.id)
|
||||
].merge(
|
||||
kwh_simulation_predictions["hotwater_kwh_predictions"].drop(
|
||||
columns=["property_id", "recommendation_id", "phase"]
|
||||
),
|
||||
how="inner",
|
||||
on="id",
|
||||
suffixes=("_heating", "_hotwater")
|
||||
).reset_index(drop=True)
|
||||
|
||||
# We adjust this table with the kwh estimates for low energy lighting kwh values, and solar kwh estimates
|
||||
led_recommendation = pd.DataFrame([
|
||||
{
|
||||
"phase": r["phase"],
|
||||
"recommendation_id": r["recommendation_id"],
|
||||
"lighting_kwh_savings": r["kwh_savings"] * GoogleSolarApi.SOLAR_CONSUMPTION_PROPORTION,
|
||||
} for recs in property_recommendations for r in recs if r["type"] == "low_energy_lighting"
|
||||
], columns=["phase", "recommendation_id", "lighting_kwh_savings"])
|
||||
solar_recommendations = pd.DataFrame([
|
||||
{
|
||||
"phase": r["phase"],
|
||||
"recommendation_id": r["recommendation_id"],
|
||||
"solar_kwh_savings": r["initial_ac_kwh_per_year"] * GoogleSolarApi.SOLAR_CONSUMPTION_PROPORTION,
|
||||
} for recs in property_recommendations for r in recs if r["type"] == "solar_pv"
|
||||
], columns=["phase", "recommendation_id", "solar_kwh_savings"])
|
||||
|
||||
# merge them on
|
||||
kwh_impact_table = kwh_impact_table.merge(
|
||||
led_recommendation, how="left", on=["phase", "recommendation_id"]
|
||||
).merge(
|
||||
solar_recommendations, how="left", on=["phase", "recommendation_id"]
|
||||
)
|
||||
|
||||
property_kwh = property_instance.energy_consumption_estimates["unadjusted"]
|
||||
|
||||
kwh_impact_table = pd.concat(
|
||||
[
|
||||
pd.DataFrame(
|
||||
[
|
||||
{
|
||||
"id": STARTING_DUMMY_ID_VALUE,
|
||||
"phase": STARTING_DUMMY_ID_VALUE,
|
||||
"recommendation_id": STARTING_DUMMY_ID_VALUE,
|
||||
"predictions_heating": property_kwh["heating"],
|
||||
"predictions_hotwater": property_kwh["hot_water"],
|
||||
}
|
||||
]
|
||||
),
|
||||
kwh_impact_table
|
||||
]
|
||||
).sort_values(["phase", "recommendation_id"], ascending=True).reset_index(drop=True)
|
||||
|
||||
for i in range(0, len(kwh_impact_table)):
|
||||
current_phase = kwh_impact_table.loc[i, 'phase']
|
||||
previous_phase_id = (current_phase - 1) if (current_phase > 0) else -9999
|
||||
previous_phase = kwh_impact_table[kwh_impact_table['phase'] == previous_phase_id]
|
||||
|
||||
if not previous_phase.empty:
|
||||
for col in ["predictions_heating", "predictions_hotwater"]:
|
||||
if kwh_impact_table.loc[i, col] > previous_phase[col].max():
|
||||
kwh_impact_table.loc[i, col] = previous_phase[col].max()
|
||||
|
||||
# For heating system recommendations, this could result in a fuel type change so we reflect that
|
||||
fuel_mapping = pd.DataFrame([
|
||||
{
|
||||
"id": epc["id"],
|
||||
**cls.map_descriptions_to_fuel(epc["mainheat-description"], epc["hotwater-description"])
|
||||
} for epc in property_instance.updated_simulation_epcs
|
||||
])
|
||||
|
||||
fuel_mapping = pd.concat(
|
||||
[
|
||||
pd.DataFrame(
|
||||
[
|
||||
{
|
||||
"id": STARTING_DUMMY_ID_VALUE,
|
||||
**cls.map_descriptions_to_fuel(
|
||||
property_instance.data["mainheat-description"],
|
||||
property_instance.data["hotwater-description"]
|
||||
)
|
||||
}
|
||||
]
|
||||
),
|
||||
fuel_mapping
|
||||
]
|
||||
)
|
||||
|
||||
kwh_impact_table = kwh_impact_table.merge(
|
||||
fuel_mapping, how="left", on="id"
|
||||
).sort_values(["phase", "recommendation_id"], ascending=True).reset_index(drop=True)
|
||||
|
||||
kwh_impact_table["heating_fuel_type"] = np.where(
|
||||
kwh_impact_table["id"] == STARTING_DUMMY_ID_VALUE,
|
||||
property_instance.heating_energy_source,
|
||||
kwh_impact_table["heating_fuel_type"]
|
||||
)
|
||||
|
||||
kwh_impact_table["hotwater_fuel_type"] = np.where(
|
||||
kwh_impact_table["id"] == STARTING_DUMMY_ID_VALUE,
|
||||
property_instance.hot_water_energy_source,
|
||||
kwh_impact_table["hotwater_fuel_type"]
|
||||
)
|
||||
|
||||
# We now calculate the fuel cost
|
||||
for k in ["heating", "hotwater"]:
|
||||
kwh_impact_table[f"{k}_cost"] = kwh_impact_table.apply(
|
||||
lambda x: AnnualBillSavings.calculate_recommendation_fuel_cost(
|
||||
x[f"adjusted_{k}"], x[f"{k}_fuel_type"], x[f"{k}_cop"]
|
||||
), axis=1
|
||||
)
|
||||
|
||||
# We now deduce if any of the recommendations result in a change of fuel type
|
||||
for recs in property_recommendations:
|
||||
for rec in recs:
|
||||
if rec["type"] == "mechanical_ventilation":
|
||||
continue
|
||||
|
||||
rec_impact = kwh_impact_table[kwh_impact_table["recommendation_id"] == rec["recommendation_id"]]
|
||||
prevous_phase_id = (rec["phase"] - 1) if (rec["phase"] > 0) else STARTING_DUMMY_ID_VALUE
|
||||
previous_phase_impact = kwh_impact_table[kwh_impact_table["phase"] == prevous_phase_id]
|
||||
|
||||
if rec["type"] == "solar_pv":
|
||||
rec["kwh_savings"] = rec_impact["solar_kwh_savings"].values[0]
|
||||
rec["energy_cost_savings"] = (
|
||||
rec_impact["solar_kwh_savings"].values[0] * AnnualBillSavings.ELECTRICITY_PRICE_CAP
|
||||
)
|
||||
continue
|
||||
|
||||
heating_kwh_savings = (
|
||||
previous_phase_impact["adjusted_heating"].mean() - rec_impact["adjusted_heating"].values[0]
|
||||
)
|
||||
heating_cost_savings = (
|
||||
previous_phase_impact["heating_cost"].mean() - rec_impact["heating_cost"].values[0]
|
||||
)
|
||||
|
||||
hotwater_kwh_savings = (
|
||||
previous_phase_impact["adjusted_hotwater"].mean() - rec_impact["adjusted_hotwater"].values[0]
|
||||
)
|
||||
hotwater_host = (
|
||||
previous_phase_impact["hotwater_cost"].mean() - rec_impact["hotwater_cost"].values[0]
|
||||
)
|
||||
|
||||
total_kwh_savings = heating_kwh_savings + hotwater_kwh_savings
|
||||
energy_cost_savings = heating_cost_savings + hotwater_host
|
||||
|
||||
if rec["type"] == "lighting":
|
||||
# In this case, we should probably just SKIP but check when we have one!
|
||||
raise Exception("Implement me 3")
|
||||
|
||||
rec["kwh_savings"] = total_kwh_savings
|
||||
rec["energy_cost_savings"] = energy_cost_savings
|
||||
|
||||
# Finally, we set the current energy bill
|
||||
starting_figures = kwh_impact_table[kwh_impact_table["id"] == STARTING_DUMMY_ID_VALUE].squeeze()
|
||||
gas_standing_charge = 0
|
||||
if (
|
||||
(starting_figures["heating_fuel_type"] == "Natural Gas") or
|
||||
(starting_figures["hotwater_fuel_type"] == "Natural Gas")
|
||||
):
|
||||
gas_standing_charge = AnnualBillSavings.DAILY_STANDARD_CHARGE_GAS * 365
|
||||
|
||||
electricity_standing_charge = AnnualBillSavings.DAILY_STANDARD_CHARGE_ELECTRICITY * 365
|
||||
|
||||
current_energy_bill = (
|
||||
starting_figures["heating_cost"].values[0] +
|
||||
starting_figures["hotwater_cost"].values[0] +
|
||||
property_instance.energy_cost_estimates["unadjusted"]["lighting"] +
|
||||
property_instance.energy_cost_estimates["unadjusted"]["appliances"] +
|
||||
gas_standing_charge + electricity_standing_charge
|
||||
)
|
||||
|
||||
return current_energy_bill
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue