internalconstant

Overview

Handles the internal gains calculations and manipulations in relation to weather data. This class is responsible for processing, generating, and managing internal gains data, which could be derived from constants or referenced time series data. The class extends AuxiliaryMethod to utilize its auxiliary functionalities and is designed to be used in scenarios where internal gains need to be modeled or analyzed.

Note: the value produced here is the sensible portion of internal gains (the heat flux that raises air temperature). The latent portion — moisture release from occupants, cooking, etc. — belongs in the separate gains_internal_latent[W] object key so the RC HVAC latent-cooling post-pass (issue #103) can account for it correctly.

Key facts

• Method key: InternalConstant

Requirements

Required keys (specify in objects)

• gains_internal[W]

Optional keys (specify in objects)

• None

Required data (specify in data)

• weather

Optional data (specify in data)

• None

Outputs

Summary metrics

• None

Timeseries columns

• None

Public methods

• generate

  def generate(self, obj, data):
    gains_internal = obj.get(O.GAINS_INTERNAL, DEFAULT_GAINS_INTERNAL)
    try:
        gains_internal = float(gains_internal)
    except ValueError:
        pass
    if isinstance(gains_internal, str):
        # If a string key is given, assume it's a reference to a time series
        return InternalTimeSeries().generate(obj, data)
    return self.run(**self.get_input_data(obj, data))
  

• get_input_data

  def get_input_data(self, obj, data):
    return {
        "gains_internal": obj.get(O.GAINS_INTERNAL, DEFAULT_GAINS_INTERNAL),
        "weather": data[O.WEATHER],
    }
  

• run

  def run(self, gains_internal, weather):
    return pd.DataFrame(
        {O.GAINS_INTERNAL: np.full(len(weather), gains_internal, dtype=np.float32)}, index=weather.index
    )