SHORT-TERM FORECAST OF WIND POWER BASED ON BSO-ELM-ADABOOST WITH GREY CORRELATION ANALYSIS

Wei Xia; Huang Deqi; Xie Lirong; Huang Chenchen; Zhao Shicheng; Ye Jiahao

doi:10.19912/j.0254-0096.tynxb.2020-0524

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Acta Energiae Solaris Sinica ›› 2022, Vol. 43 ›› Issue (3) : 426-432. DOI: 10.19912/j.0254-0096.tynxb.2020-0524

SHORT-TERM FORECAST OF WIND POWER BASED ON BSO-ELM-ADABOOST WITH GREY CORRELATION ANALYSIS

Ye Jiahao¹, Wei Xia¹, Huang Deqi¹, Xie Lirong¹, Huang Chenchen¹, Zhao Shicheng²

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Abstract

A novel method of short-term wind power prediction based on grey relational analysis and beetle swarm optimization extreme learning machine is proposed in this paper. Firstly, the gray correlation analysis is used to construct a training sample set to improve the correlation between historical data and forecasting information on the daily time scale. Furthermore, the beetle swarm optimization algorithm is used to optimize the extreme learning machine and find the optimal weight threshold for the extreme learning machine to improve its generalization ability. Finally, the concept of integrated learning is introduced, and multiple weak predictors of extreme learning machines are combined through adaptive enhancement algorithm learning to correct the prediction errors to realize the self-allocation and reorganization of error weights. The strong predictor of the extreme learning machine formed further improves the prediction accuracy of the model and the effectiveness of the method is verified by the actual data of a wind farm in Northwest China.

Key words

wind power / power forecast / Adaptive Boosting / grey correlation analysis / beetle swarm algorithm / extreme learning machine

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Ye Jiahao, Wei Xia, Huang Deqi, Xie Lirong, Huang Chenchen, Zhao Shicheng. SHORT-TERM FORECAST OF WIND POWER BASED ON BSO-ELM-ADABOOST WITH GREY CORRELATION ANALYSIS[J]. Acta Energiae Solaris Sinica. 2022, 43(3): 426-432 https://doi.org/10.19912/j.0254-0096.tynxb.2020-0524

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