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Joint modelling wind speed and power via Bayesian Dynamical models

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

Abstract

The relationship of dependence between wind speed and wind power variables has a degree of complexity that has motivated several scientific studies over the years. Much of this research seeks to understand the stochastic nature of both phenomena, either for the purpose of marginal analysis or for joint analyses, aiming to improve prediction of wind power. The present study proposes three dynamic Bayesian models for wind energy that account for the complexity of both variables via hierarchical structures such as temporal dependence, nonstationary behaviour, and truncation of power due to turbine specifications. This hierarchy considers wind energy modelling conditioned on wind speed and a marginal model for wind speed. This approach allows joint analysis to be carried out via univariate time series modelling. Analysis of a rich dataset from Bahia state (Brazil) indicates that the proposed models are accurate for both short-term and long-term wind power forecasts.

Suggested Citation

  • Duca, Victor E.L.A. & Fonseca, Thais C.O. & Cyrino Oliveira, Fernando Luiz, 2022. "Joint modelling wind speed and power via Bayesian Dynamical models," Energy, Elsevier, vol. 247(C).
    • Handle: RePEc:eee:energy:v:247:y:2022:i:c:s0360544222003346
    DOI: 10.1016/j.energy.2022.123431
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    1. Jung, Sungmoon & Arda Vanli, O. & Kwon, Soon-Duck, 2013. "Wind energy potential assessment considering the uncertainties due to limited data," Applied Energy, Elsevier, vol. 102(C), pages 1492-1503.
    2. Guedes, Kevin S. & de Andrade, Carla F. & Rocha, Paulo A.C. & Mangueira, Rivanilso dos S. & de Moura, Elineudo P., 2020. "Performance analysis of metaheuristic optimization algorithms in estimating the parameters of several wind speed distributions," Applied Energy, Elsevier, vol. 268(C).
    3. Gneiting, Tilmann & Larson, Kristin & Westrick, Kenneth & Genton, Marc G. & Aldrich, Eric, 2006. "Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime-Switching SpaceTime Method," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 968-979, September.
    4. Song, Zhe & Jiang, Yu & Zhang, Zijun, 2014. "Short-term wind speed forecasting with Markov-switching model," Applied Energy, Elsevier, vol. 130(C), pages 103-112.
    5. Lee Fawcett & David Walshaw, 2006. "A hierarchical model for extreme wind speeds," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(5), pages 631-646, November.
    6. Bastos, Bruno Quaresma & Cyrino Oliveira, Fernando Luiz & Milidiú, Ruy Luiz, 2021. "U-Convolutional model for spatio-temporal wind speed forecasting," International Journal of Forecasting, Elsevier, vol. 37(2), pages 949-970.
    7. Giwhyun Lee & Yu Ding & Marc G. Genton & Le Xie, 2015. "Power Curve Estimation With Multivariate Environmental Factors for Inland and Offshore Wind Farms," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 56-67, March.
    8. Celik, Ali Naci, 2004. "A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey," Renewable Energy, Elsevier, vol. 29(4), pages 593-604.
    9. Jung, Christopher & Schindler, Dirk, 2019. "Wind speed distribution selection – A review of recent development and progress," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    10. Marčiukaitis, Mantas & Žutautaitė, Inga & Martišauskas, Linas & Jokšas, Benas & Gecevičius, Giedrius & Sfetsos, Athanasios, 2017. "Non-linear regression model for wind turbine power curve," Renewable Energy, Elsevier, vol. 113(C), pages 732-741.
    11. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    12. Carrillo, C. & Obando Montaño, A.F. & Cidrás, J. & Díaz-Dorado, E., 2013. "Review of power curve modelling for wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 572-581.
    13. Hering, Amanda S. & Genton, Marc G., 2010. "Powering Up With Space-Time Wind Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 92-104.
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    Cited by:

    1. Li Yan & Zhengyu Zhu & Xiaopeng Kang & Boyang Qu & Baihao Qiao & Jiajia Huan & Xuzhao Chai, 2022. "Multi-Objective Dynamic Economic Emission Dispatch with Electric Vehicle–Wind Power Interaction Based on a Self-Adaptive Multiple-Learning Harmony-Search Algorithm," Energies, MDPI, vol. 15(14), pages 1-22, July.
    2. 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).
    3. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).

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