Abstract
Renewable energies are on the rise and their impact on the sustainability of our planet is consensual. Adequate tools for modeling and forecasting production from different sources are needed, so that management of energy resources is automatic and efficient. This work addresses this issue by a first modeling attempt of the wind power production in Continental Portugal using Stochastic Differential Equations (SDEs), based on available hourly observations. We resort to parametric SDE models proposed in the literature on wind energy research (the Ornstein–Uhlenbeck model and a transformed Ornstein–Uhlenbeck model), we estimate the model parameters, we perform the residual analysis and the short-term forecasting. We found that SDEs have produced useful results for the management of wind energy production. However, there would be an interest in evolving toward SDEs models that better explain the data in short periods of time, in order to obtain more reliable forecasts.
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Acknowledgements
This research was partially supported by CMUP (UID/MAT/00144/2019) funded by FCT (Pt) with National and European structural funds through FEDER, under partnership agreement PT2020, and by project STRIDE (NORTE-01-0145-FEDER-000033) supported by NORTE2020. The authors are very grateful to Professor Dr. Eng. Cláudio Monteiro (University of Porto, FEUP) for his suggestions, for giving the authors access to the data and details of the problem under study. The authors are very grateful to reviewer 1 for his/her anonymous contribution to improve the paper.
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Cabral, P., Milheiro–Oliveira, P. (2022). Modeling and Forecasting Wind Energy Production by Stochastic Differential Equations. In: Bispo, R., Henriques-Rodrigues, L., Alpizar-Jara, R., de Carvalho, M. (eds) Recent Developments in Statistics and Data Science. SPE 2021. Springer Proceedings in Mathematics & Statistics, vol 398. Springer, Cham. https://doi.org/10.1007/978-3-031-12766-3_9
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