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19 October 2022 Multi-time scale prediction method of electric vehicle charging load based on support vector machine
Jing Ge, Minhong Li, Hong Xie, Jinquan Bai, Lante Li, Peng Huang
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Proceedings Volume 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering; 122945G (2022) https://doi.org/10.1117/12.2641265
Event: 7th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2022), 2022, Xishuangbanna, China
Abstract
In this paper, the load forecasting model of electric vehicle charging station is established based on the support vector machine, which considers the daily load forecasting of single node and the influence of historical load of multiple time scales, and predicts and analyzes the load of charging station. The prediction model mainly uses the regression prediction model of support vector machine to predict the short-term load of electric vehicle charging station, then trains the model with the processed data, and finally tests the collected data at multiple time scales through experimental analysis. The results show that the average error of the prediction method proposed in this paper is less than 4% under two conditions. This proves the effectiveness and feasibility of the method in this paper.
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Jing Ge, Minhong Li, Hong Xie, Jinquan Bai, Lante Li, and Peng Huang "Multi-time scale prediction method of electric vehicle charging load based on support vector machine", Proc. SPIE 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, 122945G (19 October 2022); https://doi.org/10.1117/12.2641265
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KEYWORDS
Data modeling
Computer simulations
Mathematical modeling
Optimization (mathematics)
Performance modeling
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