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Ultra-short-term wind speed forecasting using an optimized artificial intelligence algorithm

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  • Wang, Jian
  • Yang, Zhongshan

Abstract

Accurate and stable ultra-short-term wind speed prediction is very valuable for the dispatch planning and operational security for the wind power system, however it’s very difficult to obtain satisfactory forecasting results in the wind power system due to the complexity and non-linearity of the wind speed series. In this paper, a novel hybrid model combined multi-objective optimization, data preprocessing technology and Elman neural network was proposed to forecast ultra-short-term wind speed, including 30min and 10min wind speed. To obtain better forecasting results with high accuracy and strong stability, multi-objective optimization target was utilized to balance the variance and bias of the forecasted series. Complementary ensemble empirical mode decomposition was used to remove the noise in the original data and several IMFs were obtained. This paper proposed a new optimization algorithm combined adaptive wind driven optimization and modified simulated annealing to optimize initial weights and thresholds of ENN. Wind speed data from two observation sites in China was involved in this paper to verify the forecasting performance of the proposed model. The simulation results illustrate that the proposed hybrid model has the best forecasting results at all step among all related models.

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  • Wang, Jian & Yang, Zhongshan, 2021. "Ultra-short-term wind speed forecasting using an optimized artificial intelligence algorithm," Renewable Energy, Elsevier, vol. 171(C), pages 1418-1435.
    • Handle: RePEc:eee:renene:v:171:y:2021:i:c:p:1418-1435
    DOI: 10.1016/j.renene.2021.03.020
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    5. Hua Li & Zhen Wang & Binbin Shan & Lingling Li, 2022. "Research on Multi-Step Prediction of Short-Term Wind Power Based on Combination Model and Error Correction," Energies, MDPI, vol. 15(22), pages 1-21, November.
    6. Dimitrios Michos & Francky Catthoor & Dimitris Foussekis & Andreas Kazantzidis, 2024. "A CFD Model for Spatial Extrapolation of Wind Field over Complex Terrain—Wi.Sp.Ex," Energies, MDPI, vol. 17(16), pages 1-15, August.
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    9. Ding, Lin & Bai, Yulong & Liu, Ming-De & Fan, Man-Hong & Yang, Jie, 2022. "Predicting short wind speed with a hybrid model based on a piecewise error correction method and Elman neural network," Energy, Elsevier, vol. 244(PA).
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    12. Longnv Huang & Qingyuan Wang & Jiehui Huang & Limin Chen & Yin Liang & Peter X. Liu & Chunquan Li, 2022. "A Novel Hybrid Predictive Model for Ultra-Short-Term Wind Speed Prediction," Energies, MDPI, vol. 15(13), pages 1-17, July.
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    15. Li, Yanhui & Sun, Kaixuan & Yao, Qi & Wang, Lin, 2024. "A dual-optimization wind speed forecasting model based on deep learning and improved dung beetle optimization algorithm," Energy, Elsevier, vol. 286(C).

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