A simplified seasonal forecasting strategy, applied to wind and solar power in Europe

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1016/j.cliser.2022.100318. This is version 2 of this Preprint.

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Philip Bett, Hazel E. Thornton, Alberto Troccoli, Matteo De Felice , Emma Suckling, Laurent Dubus , Yves-Marie Saint-Drenan, David Brayshaw


We demonstrate levels of skill for forecasts of seasonal-mean wind speed and solar irradiance in Europe, using seasonal forecast systems available from the Copernicus Climate Change Service (C3S). While skill is patchy, there is potential for the development of climate services for the energy sector. Following previous studies, we show that, where there is skill, a simple linear regression-based method using the hindcast and forecast ensemble means provides a straightforward approach to produce calibrated probabilistic seasonal forecasts. This method extends naturally to using a larger-scale feature of the climate, such as the North Atlantic Oscillation, as the climate model predictor, and we show that this provides opportunities to improve the skill in some cases.

We further demonstrate that, on seasonal-average and regional (e.g. national) average scales, wind and solar power generation are highly correlated with single climate variables (wind speed and irradiance). The detailed non-linear transformations from meteorological quantities to energy quantities, which are essential for detailed simulation of power system operations, are usually not necessary when forecasting gross wind or solar generation potential at seasonal-mean regional-mean scales.

Together, our results demonstrate that where there is skill in seasonal forecasts of wind speed and irradiance, or a correlated larger-scale climate predictor, skilful forecasts of seasonal mean wind and solar power generation can be made based on the climate variable alone, without requiring complex transformations. This greatly simplifies the process of developing a useful seasonal climate service.




Applied Statistics, Earth Sciences, Environmental Sciences, Physical Sciences and Mathematics, Physics, Probability, Statistics and Probability, Sustainability


seasonal forecasting, climate services, renewable energy, solar, wind


Published: 2019-04-01 20:52

Last Updated: 2021-06-12 00:16

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CC BY Attribution 4.0 International