solarforecastarbiter.datamodel.ForecastAggregate¶

class solarforecastarbiter.datamodel.ForecastAggregate(forecast: solarforecastarbiter.datamodel.Forecast, aggregate: solarforecastarbiter.datamodel.Aggregate, reference_forecast: Optional[solarforecastarbiter.datamodel.Forecast] = None, normalization: Optional[float] = None, uncertainty: Optional[float] = None, cost: Optional[str] = None)[source]¶

Class for pairing Forecast and Aggregate objects for evaluation.

Parameters:

forecast (solarforecastarbiter.datamodel.Forecast) –
aggregate (solarforecastarbiter.datamodel.Aggregate) –
reference_forecast (solarforecastarbiter.datamodel.Forecast or None) –
normalization (float or None) – If None, assigned 1.
uncertainty (None, float, or str) – If None, uncertainty is not accounted for. Float specifies the uncertainty as a percentage from 0 to 100%. Strings must be coerceable to a float.
cost (str or None) – Cost parameters to use from the costs associated with ReportParameters

__init__(forecast: solarforecastarbiter.datamodel.Forecast, aggregate: solarforecastarbiter.datamodel.Aggregate, reference_forecast: Optional[solarforecastarbiter.datamodel.Forecast] = None, normalization: Optional[float] = None, uncertainty: Optional[float] = None, cost: Optional[str] = None) → None¶

Methods

`__init__`(forecast, aggregate, …)
`from_dict`(input_dict[, raise_on_extra])	Construct a dataclass from the given dict, matching keys with the class fields.
`replace`(**kwargs)	Convience wrapper for `dataclasses.replace()` to create a new dataclasses from the old with the given keys replaced.
`to_dict`()	Convert the dataclass into a dictionary suitable for uploading to the API.

Attributes

`cost`
`normalization`
`reference_forecast`
`uncertainty`