Disaggregation

Pipeline

heatpro.disaggregation.pipeline.compose(functions: list[Callable]) → Callable

Compose a list of functions into a single function.

This function takes a list of functions and returns a new function that applies each function in the list sequentially.

Parameters:

functions (list[Callable]) – A list of functions to be composed.

Returns:

A composed function that applies each function in the list.

Return type:

Callable

heatpro.disaggregation.pipeline.disaggregate_temporal_demand(input_aggregate_demand: TemporalHeatDemand, functions: list[Callable[[TemporalHeatDemand], TemporalHeatDemand]]) → TemporalHeatDemand

Disaggregate temporal heat demand using a list of functions.

This function applies a list of functions to disaggregate a given temporal heat demand. If the list of functions is empty, the original demand is returned unchanged.

Parameters:

• input_aggregate_demand (TemporalHeatDemand) – The input aggregate demand to be disaggregated.

• functions (list[Callable[[TemporalHeatDemand],TemporalHeatDemand]]) – A list of functions to be applied for disaggregation.

Returns:

The disaggregated temporal heat demand.

Return type:

TemporalHeatDemand

Weighted Disaggregation

heatpro.disaggregation.weighted_disaggregation.daily_weighted_dissagregate(monthly_demand: MonthlyHeatDemand, weights: DataFrame, keep_month_data: bool = True) → DailyHeatDemand

Disaggregate monthly heat demand into daily values using weights.

Parameters:

• monthly_demand (MonthlyHeatDemand) – The input monthly heat demand to be disaggregated.

• weights (pd.DataFrame) – DataFrame containing weights for each day.

• keep_month_data (bool, optional) – If True, include monthly data in the output. Defaults to True.

Raises:

• ValueError – If monthly_demand is not an instance of MonthlyHeatDemand.

• ValueError – If the weight format is not valid.

• ValueError – If weights and monthly_demand do not match the same months.

Returns:

The disaggregated daily heat demand.

Return type:

DailyHeatDemand

heatpro.disaggregation.weighted_disaggregation.hourly_weighted_dissagregate(daily_demand: DailyHeatDemand, weights: DataFrame, keep_month_data: bool = True) → HourlyHeatDemand

Disaggregate daily heat demand into hourly values using weights.

Parameters:

• daily_demand (DailyHeatDemand) – The input daily heat demand to be disaggregated.

• weights (pd.DataFrame) – DataFrame containing weights for each hour.

• keep_month_data (bool, optional) – If True, include monthly data in the output. Defaults to True.

Raises:

• ValueError – If daily_demand is not an instance of DailyHeatDemand.

• ValueError – If the weight format is not valid.

• ValueError – If weights and daily_demand do not match the same months.

Returns:

The disaggregated hourly heat demand.

Return type:

HourlyHeatDemand

heatpro.disaggregation.weighted_disaggregation.monthly_weighted_disaggregate(yearly_demand: YearlyHeatDemand, weights: DataFrame, keep_year_data: bool = True) → MonthlyHeatDemand

Disaggregate yearly heat demand into monthly values using weights.

Parameters:

• yearly_demand (YearlyHeatDemand) – The input yearly heat demand to be disaggregated.

• weights (pd.DataFrame) – DataFrame containing weights for each month.

• keep_year_data (bool, optional) – If True, include yearly data in the output. Defaults to True.

Raises:

• ValueError – If yearly_demand is not an instance of YearlyHeatDemand.

• ValueError – If the weight format is not valid.

• ValueError – If there are duplicate months in the weights index.

• ValueError – If yearly_demand and weights do not overlap on the same year.

Returns:

The disaggregated monthly heat demand.

Return type:

MonthlyHeatDemand

heatpro.disaggregation.weighted_disaggregation.weekly_weighted_disaggregate(monthly_demand: MonthlyHeatDemand, weights: DataFrame, keep_year_data: bool = True) → HourlyHeatDemand

Disaggregate monthly heat demand into hourly values using weights.

Parameters:

• monthly_demand (MonthlyHeatDemand) – The input monthly heat demand to be disaggregated.

• weights (pd.DataFrame) – DataFrame containing weights for each week.

• keep_year_data (bool, optional) – If True, include yearly data in the output. Defaults to True.

Raises:

• ValueError – If monthly_demand is not an instance of MonthlyHeatDemand.

• ValueError – If the weight format is not valid.

• ValueError – If weights and monthly_demand do not match the same months.

Returns:

The disaggregated hourly heat demand.

Return type:

HourlyHeatDemand