solarforecastarbiter.reference_forecasts.persistence.persistence_scalar¶
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solarforecastarbiter.reference_forecasts.persistence.
persistence_scalar
(observation, data_start, data_end, forecast_start, forecast_end, interval_length, interval_label, load_data)[source]¶ Make a persistence forecast using the mean value of the observation from data_start to data_end.
In the example below, we use GHI to be concrete but the concept applies to any kind of observation data. The persistence forecast is:
\[GHI_{t_f} = \overline{GHI_{t_{start}} \ldots GHI_{t_{end}}}\]where \(t_f\) is a forecast time, and the overline represents the average of all observations that occur between \(t_{start}\) = data_start and \(t_{end}\) = data_end.
Parameters: - observation : datamodel.Observation
- data_start : pd.Timestamp
Observation data start. Forecast is inclusive of this instant if observation.interval_label is beginning or instantaneous.
- data_end : pd.Timestamp
Observation data end. Forecast is inclusive of this instant if observation.interval_label is ending or instantaneous.
- forecast_start : pd.Timestamp
Forecast start. Forecast is inclusive of this instant if interval_label is beginning or instantaneous.
- forecast_end : pd.Timestamp
Forecast end. Forecast is inclusive of this instant if interval_label is ending or instantaneous.
- interval_length : pd.Timedelta
Forecast interval length
- interval_label : str
instantaneous, beginning, or ending
- load_data : function
A function that loads the observation data. Must have the signature load_data(observation, data_start, data_end) and properly account for observation interval label.
Returns: - forecast : pd.Series
The persistence forecast.