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Merged
daryaguettler merged 3 commits into from
Oct 3, 2025
Merged

update to add solar as a negative end use #34

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808fc10
update to add solar as a negative end use
daryaguettler Oct 3, 2025
b488aaa
update exclusion method
daryaguettler Oct 3, 2025
aafbc1f
update emissions calc with exclusion
daryaguettler Oct 3, 2025
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139 changes: 101 additions & 38 deletions epengine/models/inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -61,7 +61,8 @@
)
from epengine.models.transforms import CategoricalFeature, RegressorInputSpec

END_USES = ("Lighting", "Equipment", "DomesticHotWater", "Heating", "Cooling")
END_USES = ("Lighting", "Equipment", "DomesticHotWater", "Heating", "Cooling", "Solar")
RAW_END_USES = ("Lighting", "Equipment", "DomesticHotWater", "Heating", "Cooling")
OIL_HEATING_SYSTEMS = ["OilHeating"]
NG_HEATING_SYSTEMS = ["NaturalGasHeating", "NaturalGasCondensingHeating"]
INCOME_BRACKETS = [
Expand All @@ -85,7 +86,7 @@
_has_warned_max_solar_capacity_cap = False

DATASET_SEGMENT_MAP = {
"Raw": END_USES,
"Raw": RAW_END_USES,
"EndUse": END_USES,
"Fuel": FUELS,
"EndUseCost": END_USES,
Expand Down Expand Up @@ -189,22 +190,16 @@ def serialized(self) -> BaseModel: # noqa: C901
field_datas = {}
percentile_mapper = {v[1]: v[0] for v in PERCENTILES.values()}

# Create copies of summary dataframes with percentile mapper applied
costs_summary_renamed = self.costs_summary.rename(index=percentile_mapper)
# paybacks_summary_renamed = self.paybacks_summary.rename(index=percentile_mapper)

# Process all numeric columns in the costs dataframe
for col in self.costs.columns:
# Skip non-numeric columns (e.g., metadata objects)
if not pd.api.types.is_numeric_dtype(self.costs[col]):
continue
# Skip detailed cost columns like cost.Trigger.Final (keep only cost.Trigger)
if col.startswith("cost.") and col.count(".") > 1:
continue
col_name = col.split(".")[-1]
field_specs[col_name] = (SummarySpec, Field(title=col))

# Get summary data for this column
if col in costs_summary_renamed.columns:
field_data = costs_summary_renamed.loc[:, col].to_dict()
else:
Expand Down Expand Up @@ -295,6 +290,16 @@ def create_end_use_disaggregation_spec(SummarySpec: type[SummarySpecBase]):
)


def create_raw_end_use_disaggregation_spec(SummarySpec: type[SummarySpecBase]):
"""Create a raw end use disaggregation spec WITHOUT Solar as a field."""
fields = {}
for end_use in RAW_END_USES:
fields[end_use] = (SummarySpec, Field(title=end_use))
return create_model(
"RawEndUseDisaggregationSpec", **fields, __config__=ConfigDict(extra="forbid")
)


def create_fuel_disaggregation_spec(SummarySpec: type[SummarySpecBase]):
"""Create a fuel disaggregation spec with the fuels as fields."""
fields = {}
Expand All @@ -306,12 +311,16 @@ def create_fuel_disaggregation_spec(SummarySpec: type[SummarySpecBase]):


def create_disaggregation_spec(
EndUseDisaggregationSpec: type[BaseModel], FuelDisaggregationSpec: type[BaseModel]
EndUseDisaggregationSpec: type[BaseModel],
RawEndUseDisaggregationSpec: type[BaseModel],
FuelDisaggregationSpec: type[BaseModel],
):
"""Create a disaggregation spec with the datasets as fields."""
fields = {}
for dataset, dataset_segments in DATASET_SEGMENT_MAP.items():
if dataset_segments == END_USES:
if dataset == "Raw":
fields[dataset] = (RawEndUseDisaggregationSpec, Field(title=dataset))
elif dataset_segments == END_USES:
fields[dataset] = (EndUseDisaggregationSpec, Field(title=dataset))
elif dataset_segments == FUELS:
fields[dataset] = (FuelDisaggregationSpec, Field(title=dataset))
Expand Down Expand Up @@ -377,9 +386,10 @@ def create_sbem_inference_savings_response_spec(

SummarySpec = create_summary_spec()
EndUseDisaggregationSpec = create_end_use_disaggregation_spec(SummarySpec)
RawEndUseDisaggregationSpec = create_raw_end_use_disaggregation_spec(SummarySpec)
FuelDisaggregationSpec = create_fuel_disaggregation_spec(SummarySpec)
DisaggregationSpec = create_disaggregation_spec(
EndUseDisaggregationSpec, FuelDisaggregationSpec
EndUseDisaggregationSpec, RawEndUseDisaggregationSpec, FuelDisaggregationSpec
)
DisaggregationsSpec = create_disaggregations_spec(DisaggregationSpec)
TotalSpec = create_total_spec(SummarySpec)
Expand Down Expand Up @@ -1701,21 +1711,43 @@ def separate_fuel_based_end_uses(
)

gas = pd.concat(
[heat_gas, cool_gas, dhw_gas, lighting * 0, equipment * 0],
[
heat_gas,
cool_gas,
dhw_gas,
lighting * 0,
equipment * 0,
],
axis=1,
keys=["Heating", "Cooling", "Domestic Hot Water", "Lighting", "Equipment"],
keys=[
"Heating",
"Cooling",
"Domestic Hot Water",
"Lighting",
"Equipment",
],
)[df_end_uses.columns]
oil = pd.concat(
[heat_oil, cool_oil, dhw_oil, lighting * 0, equipment * 0],
[
heat_oil,
cool_oil,
dhw_oil,
lighting * 0,
equipment * 0,
],
axis=1,
keys=["Heating", "Cooling", "Domestic Hot Water", "Lighting", "Equipment"],
keys=[
"Heating",
"Cooling",
"Domestic Hot Water",
"Lighting",
"Equipment",
],
)[df_end_uses.columns]

# Store actual electricity consumption for solar calculations
self._actual_electricity_consumption = actual_electricity_consumption

# Use net electricity consumption for the main fuel disaggregation

df_disaggregated_fuels = pd.concat(
[actual_electricity_consumption, net_electricity_consumption, gas, oil],
axis=1,
Expand Down Expand Up @@ -1755,8 +1787,21 @@ def compute_costs(
keys=["Electricity", "NetElectricity", "NaturalGas", "Oil"],
names=["Fuel", "EndUse"],
)
end_use_costs = disaggregated_costs.T.groupby(level=["EndUse"]).sum().T
fuel_costs = disaggregated_costs.T.groupby(level=["Fuel"]).sum().T
base_end_use_costs = pd.concat(
[elec_costs, gas_costs, oil_costs],
axis=1,
keys=["Electricity", "NaturalGas", "Oil"],
names=["Fuel", "EndUse"],
)
end_use_costs = cast(
pd.DataFrame,
base_end_use_costs.groupby(level="EndUse", axis=1).sum(),
)
solar_cost_total = net_elec_costs.sum(axis=1) - elec_costs.sum(axis=1)
end_use_costs["Solar"] = solar_cost_total
fuel_costs = cast(
pd.DataFrame, disaggregated_costs.T.groupby(level=["Fuel"]).sum().T
)

return fuel_costs, end_use_costs

Expand Down Expand Up @@ -1794,8 +1839,25 @@ def compute_emissions(
keys=["Electricity", "NetElectricity", "NaturalGas", "Oil"],
names=["Fuel", "EndUse"],
)
end_use_emissions = disaggregated_emissions.T.groupby(level=["EndUse"]).sum().T
fuel_emissions = disaggregated_emissions.T.groupby(level=["Fuel"]).sum().T
allowed_end_use_emissions = pd.concat(
[elec_emissions, gas_emissions, oil_emissions],
axis=1,
keys=["Electricity", "NaturalGas", "Oil"],
names=["Fuel", "EndUse"],
)
end_use_emissions = cast(
pd.DataFrame,
allowed_end_use_emissions.groupby(level="EndUse", axis=1).sum(),
)
solar_emissions_total = net_elec_emissions.sum(axis=1) - elec_emissions.sum(
axis=1
)
end_use_emissions["Solar"] = solar_emissions_total

fuel_emissions = cast(
pd.DataFrame,
disaggregated_emissions.groupby(level="Fuel", axis=1).sum(),
)

return fuel_emissions, end_use_emissions

Expand All @@ -1817,6 +1879,11 @@ def compute_distributions(self, features: pd.DataFrame, results_raw: pd.DataFram
results_disaggregated_fuels = self.separate_fuel_based_end_uses(
df_features=features, df_end_uses=results_end_uses
)
solar_end_use_total = results_disaggregated_fuels["NetElectricity"].sum(
axis=1
) - results_disaggregated_fuels["Electricity"].sum(axis=1)
results_end_uses = results_end_uses.copy()
results_end_uses["Solar"] = solar_end_use_total

results_fuels = results_disaggregated_fuels.T.groupby(level=["Fuel"]).sum().T
results_fuel_costs, results_end_use_costs = self.compute_costs(
Expand Down Expand Up @@ -1870,28 +1937,24 @@ def compute_distributions(self, features: pd.DataFrame, results_raw: pd.DataFram
total_keys: list[str] = []

for dataset in datasets_for_totals:
if dataset == "FuelCost":
# Sum only NetElectricity + NaturalGas + Oil to avoid double-counting Electricity
if "FuelCost" in disaggregated.columns.get_level_values("Dataset"):
fc = disaggregated.loc[:, ("FuelCost", slice(None))]
# keep only the relevant fuels if present
fuels = [
# Default: sum all segments for the dataset if present
if dataset in disaggregated.columns.get_level_values("Dataset"):
cols = disaggregated.xs(dataset, level="Dataset", axis=1)
if dataset in ("FuelCost", "FuelEmissions"):
allowed = [
c
for c in fc.columns.get_level_values("Segment")
for c in cols.columns
if c in ("NetElectricity", "NaturalGas", "Oil")
]
if fuels:
s = fc.loc[:, (slice(None), fuels)].sum(axis=1)
else:
s = pd.Series(0.0, index=disaggregated.index)
s = (
cols.loc[:, allowed].sum(axis=1)
if len(allowed) > 0
else pd.Series(0.0, index=disaggregated.index)
)
else:
s = pd.Series(0.0, index=disaggregated.index)
s = cols.sum(axis=1)
else:
# Default behavior: sum all segments for the dataset
if dataset in disaggregated.columns.get_level_values("Dataset"):
s = disaggregated.xs(dataset, level="Dataset", axis=1).sum(axis=1)
else:
s = pd.Series(0.0, index=disaggregated.index)
s = pd.Series(0.0, index=disaggregated.index)

total_parts.append(s)
total_keys.append(dataset)
Expand Down
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