Chaotic time series wind power prediction method based on OVMD-PE and improved multi-objective state transition algorithm

Chunyu Ai, Shan He, Xiaochao Fan and Weiqing Wang

Energy, 2023, vol. 278, issue C

Abstract: As the global wind power generation capacity is constantly increasing, the problems of safe operation and utilization after grid connection are becoming more and more prominent. Aiming at the problem of low accuracy and stability of wind power time series with chaotic characteristics, a time series prediction method combining chaotic characteristic processing and neural networks is proposed. First, optimal variational mode decomposition with permutation entropy (OVMD-PE) is used to decompose the original wind power time series and overcome the disadvantage of insufficient mode aliasing encountered by empirical and integrated empirical modes. Second, an improved multi-objective state transition algorithm is proposed to determine the weight coefficients among the neural networks and improve the accuracy of the reconstructed predictive neural networks. Finally, the combined prediction method is used to study and analyze the wind power data from a wind farm in Xinjiang, China, from the perspectives of multiple scenarios and multiple time scales. The experimental results show that OVMD-PE can successfully deal with chaotic characteristics and the improved algorithm has improved the prediction accuracy. Compared with other traditional prediction models, the combined prediction model has higher robustness and stability.

Keywords: Improved multi-objective state transition algorithm; Chaotic time series; Optimal variational mode decomposition; Combination model (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:278:y:2023:i:c:s0360544223010897

DOI: 10.1016/j.energy.2023.127695

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