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Research of power load prediction based on boost clustering

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Abstract

Power load prediction which helps make the optimal decision for energy management is of great significance to the safe, reliable, and economical operation of the power system. It is also a challenging task; however, if every large customer of a special transformer is modeled and forecasted for power load, a huge amount of calculation work is needed and it is not practical. Therefore, in this study, we propose a boost clustering-based approach for the prediction of power load. The traditional k-means algorithm is enhanced, and the initial cluster centers are determined in advance instead of random selection. Then, the enhanced k-means paired with the HAC algorithm are used for the clustering of power consumption users. Next, the power load of each group is predicted after the users are clustered into the different groups, and the predicted results of each group are finally summed to obtain the prediction value of the power load. Experimental findings demonstrate the validity of the proposed procedure, and the boost clustering-based approach significantly outperforms the direct prediction approach in the empirical analysis.

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Acknowledgements

This work is partly supported by the Grants from the National Natural Science Foundation of China (No. 61672439) and the Fundamental Research Funds for the Central Universities (#20720181004). The authors wish to thank the project chance provided by Dongguan Power Supply Bureau and thank Mr. Zhang Liang-jun, the chairman of Guangzhou TipDM Intelligent Technology Co., Ltd., for valuable discussion and contribution to the successful delivery of the project. The authors would also like to thank all the editors and anonymous reviewers for their constructive advice.

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  1. School of Informatics, Xiamen University, Xiamen, 361005, China

    Junde Chen, Defu Zhang & YA Nanehkaran

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  1. Junde Chen
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  2. Defu Zhang
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  3. YA Nanehkaran
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Correspondence to Junde Chen.

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Chen, J., Zhang, D. & Nanehkaran, Y. Research of power load prediction based on boost clustering. Soft Comput 25, 6401–6413 (2021). https://doi.org/10.1007/s00500-021-05632-5

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