Abstract
With the popularization of environmental protection ideas, people are increasingly valuing low-carbon lifestyles and the economy. Electric vehicles play a crucial role in this transformation to reduce carbon emissions. However, integrating electric vehicles into the power grid poses challenges, especially the possibility of destructive load peaks, which may endanger the stability and safety of the power grid. Accurately predicting the load of electric vehicles and managing grid scheduling are crucial for solving this problem. The current solutions are mainly divided into two categories: statistics-based methods and machine learning-based methods. Statistical methods require a large amount of long-term data modeling, making data collection a significant challenge. Similarly, machine learning-based methods have good long-term prediction performance on high-quality data, but they do not perform well in terms of short-term prediction accuracy. To overcome these obstacles, a comprehensive electric vehicle charging load prediction framework is proposed, which utilizes an innovative variational autoencoder to generate adversarial networks (VAE-GAN) for data processing, Principal Component Analysis (PCA) for feature extraction, and an improved CNN-GRU model for prediction. The experimental results show that the accuracy of short-term power load prediction is significantly improved, which verifies the effectiveness of the framework in processing small sample load data and provides advanced tools for intelligent management of electric vehicle charging stations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cho, K., et al.: Learning phrase representations using RNN encoder–decoder for statistical machine translation. arXiv:1406.1078 (2014)
Gao, Q., et al.:. Charging load forecasting of electric vehicle based on monte carlo and deep learning. In: 2019 IEEE Sustainable Power and Energy Conference (iSPEC) (2019)
He, Z., et al.: Short-term power load forecasting of multi-charging piles based on improved gate recurrent unit. IEEE Access 12, 2490–2499 (2023)
Hu, X., Ferrera, E., Tomasi, R., Pastrone, C.: Short-term load forecasting with radial basis functions and singular spectrum analysis for residential electric vehicles recharging control. In: 2015 IEEE 15th International Conference on Environment and Electrical Engineering (EEEIC), pp. 1783–1788 (2015)
Khan, H., Khan, M.J., Qayyum, A.: Neural network-based load forecasting model for efficient charging of electric vehicles. In: 2022 7th Asia Conference on Power and Electrical Engineering (ACPEE), pp. 2068–2072 (2022)
Kiranyaz, S., Avci, O., Abdeljaber, O., Ince, T., Gabbouj, M., Inman, D.J.: 1d convolutional neural networks and applications: a survey. Mech. Syst. Signal Process. 151, 107398 (2021)
Yi, L., Ting, X.T., Song, W., Yun, G., Hui, H., Ziwen, Q.: The load forecasting of charging stations based on support vector regression. In: 2023 5th International Conference on Power and Energy Technology (ICPET), pp. 991–995 (2023)
Madhukumar, M., Sebastian, A., Liang, X., Jamil, M., Shabbir, M.N.S.K.: Regression model-based short-term load forecasting for university campus load. IEEE Access 10, 8891–8905 (2022)
Meng, Z., Xie, Y., Sun, J.: Short-term load forecasting using neural attention model based on EMD. Electr. Eng. 104, 1857–1866 (2021)
Su, Z., et al.: Short-term load prediction of electric vehicle charging station based on long-short-term memory neural network. In: 2023 4th International Conference on Computer Engineering and Intelligent Control (ICCEIC), pp. 595–599 (2023)
Sun, X., Ouyang, Z., Yue, D.: Short-term load forecasting based on multivariate linear regression. In: 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2), pp. 1–5 (2017)
Xie, Z., Wang, R., Wu, Z., Liu, T.: Short-term power load forecasting model based on fuzzy neural network using improved decision tree. In: 2019 IEEE Sustainable Power and Energy Conference (iSPEC), pp. 482–486 (2019)
Xu, L., Chen, Y., Wang, Y., Li, N., Zong, Q., Chen, K.: Research on electric vehicle ownership and load forecasting methods. In: 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE), pp. 184–188 (2020)
Yan, W., Li, N., Shen, Y., Shi, L., Hu, B., Zhou, Z.: Charging load prediction of electric vehicles based on CNN-GAN and semi-supervised regression 42(2), 83–89
Zhao, Y., et al.: Electric vehicle charging load prediction method based on nonlinear auto-regressive neural networks. In: 2023 4th International Conference on Computer Engineering and Intelligent Control (ICCEIC), pp. 600–605 (2023)
Zhu, J., Yang, Z., Guo, Y., Zhang, J., Yang, H.: Short-term load forecasting for electric vehicle charging stations based on deep learning approaches. Appl. Sci. 9(9), 1723 (2019)
Acknowledgement
This paper is supported by the science and technology project of State Grid Corporation of China: “Research on demand-side flexible resources portrait and aggregation technology based on multi-source data fusion” (Grand No.5700-202358310A-1-1-ZN)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhang, W., Lei, S., Jiang, Y., Yao, T., Wang, Y., Sun, Z. (2025). Enhancing Load Forecasting with VAE-GAN-Based Data Cleaning for Electric Vehicle Charging Loads. In: Morishima, A., Li, G., Ishikawa, Y., Amer-Yahia, S., Jagadish, H.V., Lu, K. (eds) Database Systems for Advanced Applications. DASFAA 2024 International Workshops. DASFAA 2024. Lecture Notes in Computer Science, vol 14667. Springer, Singapore. https://doi.org/10.1007/978-981-96-0914-7_8
Download citation
DOI: https://doi.org/10.1007/978-981-96-0914-7_8
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-96-0913-0
Online ISBN: 978-981-96-0914-7
eBook Packages: Computer ScienceComputer Science (R0)