- ISSN: 2352-0965
- E-ISSN: 2352-0973
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oa A Data-driven and Deep Learning-based Mid- and Long-term Electric Vehicle Charging Load Forecasting Method
- Source: Recent Advances in Electrical & Electronic Engineering, Volume 19, Issue 1, Jan 2026, E23520965389850
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- 25 Feb 2025
- 18 Mar 2025
- 13 May 2025
Abstract
With the rapid popularization of electric vehicles, their charging load influences the stable operation of the power grid. An accurate prediction of EV charging station load is crucial for optimal resource allocation in power systems. The objective of this study is to address the issue of insufficient accuracy in existing prediction methods, this paper proposes a hybrid prediction model based on Bidirectional Long Short-Term Memory and Adaptive Boosting, aiming to improve the accuracy and stability of medium and long-term EV charging station load forecasting.
The study employs a three-step approach: (1) The pearson correlation analysis was utilized to evaluate multi-dimensional influencing factors and reduce dataset dimensionality; (2) implementation of a BiLSTM neural network for temporal feature extraction and preliminary prediction; and (3) application of the Adaboost algorithm to construct a weighted combination of strong classifiers. The model’s effectiveness was validated through comprehensive simulation tests using real-world charging station data.
The proposed Pearson feature selection-based BiLSTM-Adaboost model outperforms traditional benchmark models (LSTM and SVM), effectively reduces data redundancy through feature selection, achieves better performance in key indicators (MSE, RMSE, and MAPE), and demonstrates strong generalization ability and robustness while maintaining high accuracy.
Experimental results demonstrate that the proposed method effectively extracts key features of charging loads, achieving superior prediction accuracy and generalization ability compared to traditional methods. This provides a reliable decision-making tool for power grid operation, effectively supporting the resilience planning needs of urban power grids under continuously increasing EV penetration rates. But further research is needed to address robustness under extreme weather conditions.
This study provides an effective load forecasting methodology for power systems to address the challenges of large-scale electric vehicle integration. Future research will explore more robust feature engineering methods and deep learning architectures, such as combining other more advanced time series prediction models and improving optimization algorithms to enhance model adaptability and generalization capability for complex data patterns.
