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13 May 2024 Application of a class of density peak clustering algorithm in short-term smart grid
Chenyu Wang, Zhao Zhang, Zhipeng Zhu, Hongyan Zhou
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Proceedings Volume 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023); 131597W (2024) https://doi.org/10.1117/12.3024401
Event: Eighth International Conference on Energy System, Electricity and Power (ESEP 2023), 2023, Wuhan, China
Abstract
The classification of short-term power load data by clustering algorithm can lay a good foundation for the subsequent power load forecasting work and provide a more efficient, safe and reliable direction for the operation of power system. Density peaks clustering algorithm with k-nearest neighbors and weighted similarity can reduce the negative impact of global data on local data, avoid the collateral effect of errors and improve the accuracy of the model. It has better effect on local peak power change and global power forecast. In this paper, the density peaks clustering algorithm with k-nearest neighbors and weighted similarity are compared with DPC clustering method through simulation examples, and the effectiveness of this method is verified.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Chenyu Wang, Zhao Zhang, Zhipeng Zhu, and Hongyan Zhou "Application of a class of density peak clustering algorithm in short-term smart grid", Proc. SPIE 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023), 131597W (13 May 2024); https://doi.org/10.1117/12.3024401
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KEYWORDS
Power grids
Data modeling
Data processing
Error analysis
Power consumption
Statistical analysis
Software engineering
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