demandlib_heat

Overview

Space heating demand using demandlib’s temperature-driven BDEW model.

Purpose and scope:

• Wraps demandlib’s BDEW methodology to synthesize hourly heating energy profiles driven by outdoor air temperature and building archetype parameters. Profiles are scaled to an annual demand target and aligned to the requested timestep.

Notes:

• Provide weather with a datetime column and air temperature. The method derives wall‑clock timestamps and applies an energy‑conserving resampling when your resolution differs from the native demandlib resolution.

• Building type/class and wind class adjust the sensitivity and seasonal shape as per BDEW assumptions.

Reference:

• demandlib (BDEW heat): https://demandlib.readthedocs.io/

• BDEW guideline (German Association of Energy and Water Industries).

Key facts

• Method key: demandlib_heat

• Supported types:

  • heating

Requirements

Required keys (specify in objects)

• weather

Optional keys (specify in objects)

• demand[kWh]

• building_type

• building_class

• wind_class

• holidays_location

Required data (specify in data)

• weather

Optional data (specify in data)

• None

Outputs

Summary metrics

Key	Description
heating:demand[kWh]	total heating demand

Timeseries columns

Column	Description
heating:load[W]	heating load

Public methods

• generate

    def generate(
      self,
      obj: dict = None,
      data: dict = None,
      results: dict = None,
      ts_type: str = Types.HEATING,
      *,
      annual_demand_kwh: float = None,
      weather: pd.DataFrame = None,
      building_type: str = None,
      building_class: int = None,
      wind_class: int = None,
      holidays_location: Optional[str] = None,
  ):
      """Generate a heating load timeseries using demandlib's BDEW heat model.
  
      This method prepares inputs (including optional overrides), computes the
      heating demand profile based on outdoor air temperature and BDEW
      parameters, and returns a summary and timeseries.
  
      Args:
          obj: Object dictionary with inputs like demand, building params, weather key.
          data: Data dictionary containing the weather dataframe under O.WEATHER.
          results: Unused placeholder for interface compatibility.
          ts_type: Timeseries type, defaults to Types.HEATING.
          annual_demand_kwh: Optional annual heat demand in kWh; defaults to 1.0.
          weather: Optional weather dataframe override with C.DATETIME and C.TEMP_AIR.
          building_type: Optional BDEW building type (e.g., "EFH").
          building_class: Optional BDEW building class (int).
          wind_class: Optional BDEW wind class (int).
          holidays_location: Optional country/region code for holidays (e.g., "DE").
  
      Returns:
          dict: {"summary": {...}, "timeseries": DataFrame with column
          "HEATING|load[W]" indexed like the input weather datetimes.
      """
      # Process keyword arguments
      processed_obj, processed_data = self._process_kwargs(
          obj,
          data,
          annual_demand_kwh=annual_demand_kwh,
          weather=weather,
          building_type=building_type,
          building_class=building_class,
          wind_class=wind_class,
          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(f"[demandlib heat]: Generating {ts_type} data")
  
      return self._format_output(ts, processed_data)