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added total calcs

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This commit is contained in:
Khalim Conn-Kowlessar 2024-08-10 02:16:17 +01:00
parent 85f73eda5c
commit 8c711c9658
1 changed files with 37 additions and 3 deletions
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40
backend/app/plan/router.py
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@ -487,7 +487,8 @@ async def trigger_plan(body: PlanTriggerRequest):
data=epcs_for_scoring,
bucket=get_settings().DATA_BUCKET,
model_prefixes=["heating_kwh_predictions", "hotwater_kwh_predictions"],
extract_ids=False
extract_ids=False,
batch_size=SCORING_BATCH_SIZE
)
# Insert the spatial data
@ -797,7 +798,13 @@ async def trigger_plan(body: PlanTriggerRequest):
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}
"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):
@ -810,7 +817,12 @@ async def trigger_plan(body: PlanTriggerRequest):
"heating_cop": mapped["cop"], "hotwater_cop": mapped["cop"]
}
raise NotImplementedError("Implement me")
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([
@ -820,6 +832,9 @@ async def trigger_plan(body: PlanTriggerRequest):
} 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(
@ -911,6 +926,25 @@ async def trigger_plan(body: PlanTriggerRequest):
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
)
# 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
# possibility with heating system