.. currentmodule:: solarforecastarbiter.reference_forecasts
The Solar Forecast Arbiter supports reference forecasts based on data from numerical weather prediction (NWP) models and from site observations.
The :py:mod:`~solarforecastarbiter.reference_forecasts.main` module orchestrates reference forecast generation within the Solar Forecast Arbiter. It uses data types defined in :py:mod:`solarforecastarbiter.datamodel`.
.. autosummary:: :toctree: generated/ main main.run_nwp main.run_persistence
Forecasts based on NWP model data are used for intraday and longer forecasts. The Solar Forecast Arbiter contains a set of functions to process data from NWP forecasts. These functions are found in the :py:mod:`~solarforecastarbiter.reference_forecasts.models` module. Each function is specific to:
- A particular NWP model data set (e.g. NAM or subhourly HRRR), and
- The post processing steps required to obtain a particular type of irradiance or power forecast data (e.g. hourly mean or instantaneous).
.. autosummary:: :toctree: generated/ models models.hrrr_subhourly_to_subhourly_instantaneous models.hrrr_subhourly_to_hourly_mean models.rap_ghi_to_instantaneous models.rap_cloud_cover_to_hourly_mean models.gfs_quarter_deg_3hour_to_hourly_mean models.gfs_quarter_deg_hourly_to_hourly_mean models.gfs_quarter_deg_to_hourly_mean models.nam_12km_hourly_to_hourly_instantaneous models.nam_12km_cloud_cover_to_hourly_mean models.gefs_half_deg_to_hourly_mean
All of the above functions return weather forecast data, a resampler function, and a solar position calculation function. The weather forecast data may be supplied to a PV model and then resampled using the resampler function. This workflow allows for seperation of weather data processing and PV modeling while preserving the ability to use more accurate, shorter time interval inputs to the PV model. In the case of probabilistic forecasts, the resampler function also may define how an ensemble of deterministic forecasts should be translated to a probabilistic forecast. Finally, these functions return a solar position calculation function (rather than the actual solar position) to simplify the API while maintaining reasonable performance. (Solar position is only sometimes needed within the model processing functions and is only needed externally if power is to be calculated.) See :py:mod:`~solarforecastarbiter.reference_forecasts.models` module for additional documentation.
Many of the functions in :py:mod:`~solarforecastarbiter.reference_forecasts.models` rely on common functions related to forecast processing. These functions are found in :py:mod:`~solarforecastarbiter.reference_forecasts.forecast`.
.. autosummary:: :toctree: generated/ forecast forecast.cloud_cover_to_ghi_linear forecast.cloud_cover_to_irradiance_ghi_clear forecast.cloud_cover_to_irradiance forecast.resample forecast.reindex_fill_slice forecast.unmix_intervals forecast.sort_gefs_frame
The solarforecastarbiter supports several varieties of persistence forecasts.
.. autosummary:: :toctree: generated/ persistence persistence.persistence_scalar persistence.persistence_interval persistence.persistence_scalar_index
The Solar Forecast Arbiter runs workers that automatically create reference forecasts. This feature is currently limited to reference forecasts defined by the Arbiter itself. In the future we hope to extend this to user-defined reference forecasts.
Automated generation of reference NWP forecasts is achieved by adding a set of parameters to a Forecast's extra_parameters (formatted as JSON). These parameters are:
- is_reference_forecast - true or 'true' for automated generation
- model - string of one of the functions found in :py:mod:`~solarforecastarbiter.reference_forecasts.models`
- piggyback_on - optional, the ID of another Forecast object to group together when making forecasts. For example, if ForecastA has variable ac_power and ForecastB has variable for ghi for the same site, ForecastB piggyback_on can be set to the forecast_id of ForecastA. Then these forecasts would be grouped together and the values of ForecastB would be the same GHI values that were used in the generation of ForecastA.
An example of a valid extra_parameters JSON for automated generation is:
{
"is_reference_forecast": true,
"model": "gfs_quarter_deg_hourly_to_hourly_mean",
"piggyback_on": "da2bc386-8712-11e9-a1c7-0a580a8200ae"
}The function :py:func:`~solarforecastarbiter.reference_forecasts.main.make_latest_nwp_forecasts` is responsible for listing all forecasts available to a user and generating the appropriate reference NWP forecasts. In practice, the CLI script :py:func:`~solarforecastarbiter.cli.referencenwp` is called as a cronjob using an appropriate reference user account to continuously update pre-defined reference forecasts.
.. autosummary:: :toctree: generated/ main.make_latest_nwp_forecasts