Electric vehicle charging load forecasting based on federal learning

Yi Wu; Zhufu Shen; Yingjie Tian; Zhenfei Cai; Fan Li

doi:10.1117/12.2634646

4 May 2022 Electric vehicle charging load forecasting based on federal learning

Yi Wu, Zhufu Shen, Yingjie Tian, Zhenfei Cai, Fan Li

Proceedings Volume 12172, International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021); 1217210 (2022) https://doi.org/10.1117/12.2634646
Event: International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021), 2021, Nanchang, China

Abstract

The rapid development of the global economy has brought a lot of fossil energy consumption and environmental pollution, such as the greenhouse effect caused by car exhaust. In order to fundamentally replace the use of fossil energy, electric vehicles have been vigorously promoted by governments all over the world in recent years. However, the electric vehicle charging pile has encountered a new problem in the process of promotion: the electric vehicle charging load is often unbalanced in time and space, which requires an accurate power load forecasting and scheduling model. In the past, algorithms such as random forest were used to predict the load data of charging piles, which provides a more accurate prediction for the load data. However, these methods require a large amount of data trained by the power load model and are not conducive to the protection of privacy. In order to solve these problems, we design an FRF-CNN model, which combines federated learning with random forest and the convolutional neural network model. Extensive experiments show that FRF-CNN has better classification performance on distributed charging piles than other models, and our method effectively protects the privacy of sensitive data.

Citation Download Citation

Yi Wu, Zhufu Shen, Yingjie Tian, Zhenfei Cai, and Fan Li "Electric vehicle charging load forecasting based on federal learning", Proc. SPIE 12172, International Conference on Electronic Information Engineering and Computer Communication (EIECC 2021), 1217210 (4 May 2022); https://doi.org/10.1117/12.2634646

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