The wind power prediction based on hierarchical clustering method

Jiaying Zhang; Weiliang Zhai; Qichao Sun

doi:10.1117/12.2671659

29 March 2023 The wind power prediction based on hierarchical clustering method

Jiaying Zhang, Weiliang Zhai, Qichao Sun

Proceedings Volume 12594, Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022); 125940F (2023) https://doi.org/10.1117/12.2671659
Event: Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022), 2022, Xi'an, China

Abstract

An empirical mode decomposition-extreme learning machine (EMD-ELM) wind power prediction method based on the hierarchical clustering method was proposed to solve the current problem of insufficient power prediction accuracy of wind power stations in this paper. This method uses the aggregation algorithm of hierarchical clustering to cluster the data with similar weather conditions, and uses the EMD method to decompose the power sequence of each group, which can obtain relatively stable data components, and finally uses the ELM method to predict and combine each component. Compared with the ELM wind power prediction model, the numerical simulation shows that the EMD-ELM wind power prediction model based on hierarchical clustering method makes the data characteristics of similar weather conditions more obvious, the value of each evaluation index is better and the model has higher prediction accuracy.

Citation Download Citation

Jiaying Zhang, Weiliang Zhai, and Qichao Sun "The wind power prediction based on hierarchical clustering method", Proc. SPIE 12594, Second International Conference on Electronic Information Engineering and Computer Communication (EIECC 2022), 125940F (29 March 2023); https://doi.org/10.1117/12.2671659

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