solarforecastarbiter.reference_forecasts.main.run_nwp

solarforecastarbiter.reference_forecasts.main.run_nwp(forecast, model, run_time, issue_time)

Calculate benchmark irradiance and power forecasts for a Forecast.

Forecasts may be run operationally or retrospectively. For operational forecasts, run_time is typically set to now. For retrospective forecasts, run_time is the time by which the forecast should be run so that it could have been be delivered for the issue_time. Forecasts will only use data with timestamps before run_time.

Parameters:

• forecast (datamodel.Forecast) – The metadata of the desired forecast.
• model (function) – NWP model loading and processing function. See solarforecastarbiter.reference_forecasts.models for options.
• run_time (pd.Timestamp) – Run time of the forecast.
• issue_time (pd.Timestamp) – Issue time of the forecast run.

Returns:

• ghi (pd.Series)
• dni (pd.Series)
• dhi (pd.Series)
• air_temperature (pd.Series)
• wind_speed (pd.Series)
• ac_power (pd.Series)

Examples

The following code would return hourly average forecasts derived from the subhourly HRRR model.

>>> from solarforecastarbiter import datamodel
>>> from solarforecastarbiter.reference_forecasts import models
>>> init_time = pd.Timestamp('20190328T1200Z')
>>> start = pd.Timestamp('20190328T1300Z')  # typical available time
>>> end = pd.Timestamp('20190329T1300Z')  # 24 hour forecast
>>> modeling_parameters = datamodel.FixedTiltModelingParameters(
...     ac_capacity=10, dc_capacity=15,
...     temperature_coefficient=-0.004, dc_loss_factor=0,
...     ac_loss_factor=0)
>>> power_plant = datamodel.SolarPowerPlant(
...     name='Test plant', latitude=32.2, longitude=-110.9,
...     elevation=715, timezone='America/Phoenix',
...     modeling_parameters = modeling_parameters)
>>> ghi, dni, dhi, temp_air, wind_speed, ac_power = run(
...     power_plant, models.hrrr_subhourly_to_hourly_mean,
...     init_time, start, end)