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

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Authors

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

Abstract

We demonstrate the current levels of skill for seasonal forecasts of wind and irradiance in Europe, using 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 a simple linear regression-based method, using the hindcast and forecast ensemble means, provides a straightforward approach to produce reliable probabilistic seasonal forecasts in the cases where there is skill. This method extends naturally to using a larger-scale feature of the climate, such as the North Atlantic Oscillation, as the climate model predictor, providing opportunities to improve the skill in some cases.

We further demonstrate that taking a seasonal average and a regional (e.g. national) average means that wind and solar power generation are highly correlated with single climate variables (wind speed and irradiance): the detailed non-linear transformations from meteorological variables to energy variables, which can be essential for precision on weather forecasting timescales and for climatological studies, are usually not necessary when producing seasonal forecasts of these average quantities.

Together, our results demonstrate that, in the cases where there is skill in seasonal forecasts of wind speed and irradiance, or a correlated larger-scale climate predictor, it can be very straightforward to forecast seasonal mean wind and solar power generation based on those climate variables, without requiring complex transformations. This greatly simplifies the process of developing a useful seasonal climate service.

DOI

https://doi.org/10.31223/osf.io/kzwqx

Subjects

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

Keywords

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

Dates

Published: 2019-04-01 12:52

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License

CC BY Attribution 4.0 International

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