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A generalized dynamical model for wind speed forecasting

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  • Duca, Victor E.L.A.
  • Fonseca, Thaís C.O.
  • Cyrino Oliveira, Fernando L.

Abstract

The Weibull distribution is commonly used to model wind speed data, mainly due to its good fit to asymmetric positive variables. Several proposals have extended this approach to accommodate realistic features of wind data such as nonstationary behavior due to changes in atmospheric regimes. The present work considers wind speed modeling over time through the dynamic Weibull and Gamma state space models. Properties of both models are presented and filtering, smoothing and prediction equations are analytically obtained. Efficient simulation of scenarios is obtained through the beta prime distribution, which allows fast online forecasts of wind speed. The models are compared regarding fit and predictive performance for the analysis of two wind speed datasets in different regions of Brazil. Results indicate that the dynamic Gamma model is competitive with the Weibull model for wind prediction.

Suggested Citation

  • Duca, Victor E.L.A. & Fonseca, Thaís C.O. & Cyrino Oliveira, Fernando L., 2021. "A generalized dynamical model for wind speed forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
    • Handle: RePEc:eee:rensus:v:136:y:2021:i:c:s1364032120307085
    DOI: 10.1016/j.rser.2020.110421
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    Cited by:

    1. Liu, Ling & Wang, Jujie & Li, Jianping & Wei, Lu, 2023. "Monthly wind distribution prediction based on nonparametric estimation and modified differential evolution optimization algorithm," Renewable Energy, Elsevier, vol. 217(C).
    2. Keck, Felix & Jütte, Silke & Lenzen, Manfred & Li, Mengyu, 2022. "Assessment of two optimisation methods for renewable energy capacity expansion planning," Applied Energy, Elsevier, vol. 306(PA).
    3. Duca, Victor E.L.A. & Fonseca, Thaís C.O. & Cyrino Oliveira, Fernando Luiz, 2023. "An overview of non-Gaussian state-space models for wind speed data," Energy, Elsevier, vol. 266(C).

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