demandlib_electricity

Overview

Electricity demand using demandlib’s BDEW standard load profiles (SLPs).

This method wraps demandlib’s implementation of the German BDEW standard load profiles to generate high‑quality electricity demand time series. Given a target time horizon (Objects.DATETIMES) and an annual energy demand in kWh, it constructs the canonical 15‑minute SLP for the relevant calendar year(s), scales it to the requested annual demand, and aligns it to the target resolution using energy‑conserving resampling rules.

Key characteristics:

Profile families such as household (H0) and commercial (Gx) are supported via Objects.PROFILE; defaults to an H0 dynamic profile.
Public holidays can be considered by providing Objects.HOLIDAYS_LOCATION.
Output power is provided in Watts and indexed like the provided datetimes.

Reference:

demandlib documentation (BDEW SLPs): https://demandlib.readthedocs.io/
BDEW guideline (German Association of Energy and Water Industries).

Key facts

Method key: demandlib_electricity
Supported types:
- electricity

Requirements

Required keys (specify in objects)

datetimes

Optional keys (specify in objects)

demand[kWh]
profile
holidays_location

Required data (specify in data)

None

Optional data (specify in data)

None

Outputs

Summary metrics

Key	Description
`electricity:demand[Wh]`	total electricity demand
`electricity:load_max[W]`	maximum electricity load

Timeseries columns

Column	Description
`electricity:load[W]`	electricity load

Public methods

generate

  def generate(
    self,
    obj: dict = None,
    data: dict = None,
    results: dict = None,
    ts_type: str = Types.ELECTRICITY,
    *,
    profile: str = None,
    demand_kwh: float = None,
    weather: pd.DataFrame = None,
    holidays_location: Optional[str] = None,
) -> dict:
    """Generate an electricity demand timeseries using demandlib BDEW profiles.

    This is the public entry point for the electricity method. It accepts either
    an object/data mapping or keyword overrides, prepares inputs, computes the
    load profile at the requested resolution, and returns a summary and
    timeseries dataframe.

    Args:
        obj: Object dictionary providing inputs (e.g., demand, profile key).
        data: Data dictionary containing required timeseries (O.DATETIMES).
        results: Unused placeholder for interface compatibility.
        ts_type: Timeseries type, defaults to Types.ELECTRICITY.
        profile: Optional BDEW profile key (e.g., "h0", "g0"); defaults to
            method-level DEFAULT_PROFILE when not provided.
        demand_kwh: Optional annual demand in kWh; defaults to 1.0 if not provided.
        weather: Unused for this method; accepted for API symmetry.
        holidays_location: Optional holidays country/region code used by demandlib
            to adjust profiles (e.g., "DE").

    Returns:
        dict: A dictionary with keys:
            - "summary": Mapping with total demand [Wh] and max load [W].
            - "timeseries": DataFrame indexed like the input datetimes with one
              column "ELECTRICITY|load[W]" of integer W values.
    """
    processed_obj, processed_data = self._process_kwargs(
        obj,
        data,
        profile=profile,
        demand_kwh=demand_kwh,
        weather=weather,
        holidays_location=holidays_location,
    )
    processed_obj, processed_data = self._get_input_data(processed_obj, processed_data, ts_type)

    ts = calculate_timeseries(processed_obj, processed_data)

    logger.debug("[demandlib elec]: Generated succesfully.")

    return self._format_output(ts, processed_data)