© 2017 Royal Meteorological Society

Wind power forecasts are useful tools for power load balancing, energy trading and wind farm operations. Long range monthly-to-seasonal forecasting allows the prediction of departures from average weather conditions beyond traditional weather forecast timescales, months in advance. However, it has not yet been demonstrated how these forecasts can be optimally transformed to wind power. The predictable part of a seasonal forecast is for longer monthly averages, not daily averages, but to use monthly averages misses information on variability. To investigate, here a model relating average weather conditions to average wind power output was built, based on the relationship between instantaneous wind speed and power production and incorporating fluctuations in air density due to temperature and wind speed variability. Observed monthly average power output from UK stations was used to validate the model and to investigate the optimal temporal resolution for the data used to drive the model. Multiple simulations of wind power were performed based on reanalysis data, making separate simulations based on monthly, daily and sub-daily averages, using a distribution defined by the mean across the period to incorporate information on variability. Basing the simulation on monthly averages alone is sub-optimal: using daily average winds gives the highest correlation against observations. No improvement over this is gained by using sub-daily averages or including temperature variability. This signifies that to transform seasonal forecasts to wind power a compromise must be made between using the daily averages with debatable skill and the more predictable monthly averages, losing information on day-to-day variability.

he authors would like to acknowledge funding from EU-FP7 projects SPECS (grant agreement 308378), EUPORIAS (grant agreement 308291) and from the Spanish Ministerio de Economía y Competitividad (MINECO) project RESILIENCE (grant agreement CGL2013-41055-R). For use of and access to the SeaWind data DM would like to thank Jose Manuel Gutiérrez and the Santander Meteorology Group at the University of Cantabria.

Peer Reviewed