Ultra-Short-Term Wind Power Combined Prediction Based on Complementary Ensemble Empirical Mode Decomposition, Whale Optimisation Algorithm, and Elman Network

Anfeng Zhu, Qiancheng Zhao, Xian Wang and Ling Zhou
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Anfeng Zhu: Engineering Research Center of Hunan Province for the Mining and Utilization of Wind Turbines Operation Data, Hunan University of Science and Technology, Xiangtan 411201, China
Qiancheng Zhao: Engineering Research Center of Hunan Province for the Mining and Utilization of Wind Turbines Operation Data, Hunan University of Science and Technology, Xiangtan 411201, China
Xian Wang: Engineering Research Center of Hunan Province for the Mining and Utilization of Wind Turbines Operation Data, Hunan University of Science and Technology, Xiangtan 411201, China
Ling Zhou: Engineering Research Center of Hunan Province for the Mining and Utilization of Wind Turbines Operation Data, Hunan University of Science and Technology, Xiangtan 411201, China

Energies, 2022, vol. 15, issue 9, 1-17

Abstract: Accurate wind power forecasting helps relieve the regulation pressure of a power system, which is of great significance to the power system’s operation. However, achieving satisfactory results in wind power forecasting is highly challenging due to the random volatility characteristics of wind power sequences. This study proposes a novel ultra-short-term wind power combined prediction method based on complementary ensemble empirical mode decomposition, the whale optimization algorithm (WOA), and the Elman neural network model. The model can not only solve the phenomenon of easy modal mixing in decomposition but also avoid the problems of reconstruction error and low efficiency in the decomposition process. Furthermore, a new metaheuristic algorithm, WOA, was introduced to optimize the model and improve the accuracy of wind power prediction. Considering a wind farm as an example, several wind turbines were selected to simulate and analyse wind power by using the established prediction model, and the experimental results suggest that the proposed method has a higher prediction accuracy of ultra-short-term wind power than other prediction models.

Keywords: ultra-short-term wind power forecast; complementary ensemble empirical mode decomposition; whale optimization algorithm; combination model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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