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25 September 2023 Features analysis and prediction of electric load based on clustering and XGBoost
Shumin Sun, Jiawei Xing, Yan Cheng, Peng Yu, Yuejiao Wang, Song Yang, Sun Li, Shibai Wang
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Proceedings Volume 12788, Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023); 127884X (2023) https://doi.org/10.1117/12.3004255
Event: Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023), 2023, Kuala Lumpur, Malaysia
Abstract
Electricity load forecasting is of great significance for the stable operation of power grid. However, issues such as large amount of load data, complex historical data, and unclear feature relationships significantly affect the accuracy of load forecasting. Currently, load forecasting methods based on machine learning have become a research hotspot. In this paper, we propose a method that combines hierarchical clustering and XGBoost algorithm to improve the accuracy and efficiency of load forecasting. Firstly, the distances between loads are calculated using hierarchical clustering, and the hierarchical clustering dendrogram is obtained to characterize the similarity between loads. Then, XGBoost is used for feature analysis and selection under different cluster numbers to obtain the clustering scheme and prediction results with the minimum relative prediction error. Finally, the proposed method is validated through a case study, providing a reference for practical load forecasting in the power system.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Shumin Sun, Jiawei Xing, Yan Cheng, Peng Yu, Yuejiao Wang, Song Yang, Sun Li, and Shibai Wang "Features analysis and prediction of electric load based on clustering and XGBoost", Proc. SPIE 12788, Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023), 127884X (25 September 2023); https://doi.org/10.1117/12.3004255
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KEYWORDS
Decision trees
Education and training
Machine learning
Power grids
Analytical research
Error analysis
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