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Wind power prediction based on wind speed forecast using hidden Markov model

Author

Listed:
  • Khatereh Ghasvarian Jahromi
  • Davood Gharavian
  • Hamid Reza Mahdiani

Abstract

This study examines a new approach for short‐term wind speed and power forecasting based on the mixture of Gaussian hidden Markov models (MoG‐HMMs). The proposed approach focuses on the characteristics of wind speed and power in the consecutive hours of previous days. The proposed method is carried out in two steps. In the first step, for the hourly prediction of wind speed, several wind speed features are employed in MoG‐HMM, and in the second step, the results obtained from the first step along with their characteristics and wind power features are used to predict wind power estimation. To increase the prediction accuracy, the data used in each step are classified, and then for each class, one HMM with its specific parameters is used. The performance of the proposed approach is examined using real NREL data. The results show that the proposed method is more precise than other examined methods.

Suggested Citation

  • Khatereh Ghasvarian Jahromi & Davood Gharavian & Hamid Reza Mahdiani, 2023. "Wind power prediction based on wind speed forecast using hidden Markov model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(1), pages 101-123, January.
    • Handle: RePEc:wly:jforec:v:42:y:2023:i:1:p:101-123
    DOI: 10.1002/for.2889
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    References listed on IDEAS

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    Cited by:

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    2. Rabie, Dalia & Farzaneh, Hooman, 2026. "A novel modeling framework for demand response-based energy management systems in smart electricity markets, using optimization and multi-criteria decision making techniques," Applied Energy, Elsevier, vol. 405(C).
    3. Zhenghui Li & Zhongxiu Chen & Zhehao Huang, 2026. "Modelling the data-generating mechanism of China’s commodity market by identifying hidden information flow regimes," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 12(1), pages 1-28, December.
    4. Yang, Mao & Guo, Yunfeng & Huang, Tao & Fan, Fulin & Ma, Chenglian & Fang, Guozhong, 2024. "Wind farm cluster power prediction based on graph deviation attention network with learnable graph structure and dynamic error correction during load peak and valley periods," Energy, Elsevier, vol. 312(C).
    5. Gámiz, M.L. & Navas-Gómez, F. & Raya-Miranda, R. & Segovia-García, M.C., 2023. "Dynamic reliability and sensitivity analysis based on HMM models with Markovian signal process," Reliability Engineering and System Safety, Elsevier, vol. 239(C).

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