Method set_forecaster

src/pymgrid/microgrid/microgrid.py:527–601

Set the forecaster for timeseries modules in the microgrid. You may either pass in a single value for ``forecaster`` to apply the same forecasting logic to all timeseries modules, or pass key-value pairs in a ``dict`` to set the forecaster for specific modules. In the latte

(self,
                       forecaster,
                       forecast_horizon=DEFAULT_HORIZON,
                       forecaster_increase_uncertainty=False,
                       forecaster_relative_noise=False)

        return df.to_dict()

    def set_forecaster(self,
                       forecaster,
                       forecast_horizon=DEFAULT_HORIZON,
                       forecaster_increase_uncertainty=False,
                       forecaster_relative_noise=False):
        """
        Set the forecaster for timeseries modules in the microgrid.

        You may either pass in a single value for ``forecaster`` to apply the same forecasting logic to all timeseries
        modules, or pass key-value pairs in a ``dict`` to set the forecaster for specific modules. In the latter case,
        you may also set different forecasters for each named module. See :meth:`.get_forecaster` for additional details
        on setting forecasters.

        forecaster : callable, float, "oracle", None, or dict.
            Function that gives a forecast n-steps ahead.

            * If ``callable``, must take as arguments ``(val_c: float, val_{c+n}: float, n: int)``, where

              * ``val_c`` is the current value in the time series: ``self.time_series[self.current_step]``

              * ``val_{c+n}`` is the value in the time series n steps in the future

              * n is the number of steps in the future at which we are forecasting.

              The output ``forecast = forecaster(val_c, val_{c+n}, n)`` must have the same sign
              as the inputs ``val_c`` and ``val_{c+n}``.

            * If ``float``, serves as a standard deviation for a mean-zero gaussian noise function
              that is added to the true value.

            * If ``"oracle"``, gives a perfect forecast.

            * If ``None``, no forecast.

            * If ``dict``, must contain key-value pairs of the form ``module_name: forecaster``.
              Will set the forecaster of the module corresponding to ``module_name`` using the logic above.


        forecast_horizon : int
            Number of steps in the future to forecast. If forecaster is None, this parameter is ignored and the resultant
            horizon will be zero.

        forecaster_increase_uncertainty : bool, default False
            Whether to increase uncertainty for farther-out dates if using a GaussianNoiseForecaster. Ignored otherwise.

        forecaster_relative_noise : bool, default False
            Whether to define noise standard deviation relative to mean of time series if using
            :class:`.GaussianNoiseForecaster`. Ignored otherwise.

        """
        if isinstance(forecaster, dict):
            for module_name, _forecaster in forecaster.items():
                if module_name not in self._modules.names():
                    raise NameError(f'Unrecognized module {module_name}.')

                try:
                    self._modules[module_name].set_forecaster(
                        _forecaster,