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20 pages, 2189 KB  
Article
Enhanced Deep Representation Learning Extreme Learning Machines for EV Charging Load Forecasting by Improved Artemisinin Optimization and Multivariate Variational Mode Decomposition
by Anjie Zhong, Honghai Li, Zhongyi Tang and Zhirong Zhang
Energies 2025, 18(22), 6061; https://doi.org/10.3390/en18226061 - 20 Nov 2025
Cited by 1 | Viewed by 677
Abstract
The Electric Vehicle (EV) industry is developing rapidly, and EVs are becoming an increasingly important choice for the future of transportation. Therefore, accurately forecasting the electricity demand for EVs is crucial. This paper presents a hybrid deep learning model for EV charging load [...] Read more.
The Electric Vehicle (EV) industry is developing rapidly, and EVs are becoming an increasingly important choice for the future of transportation. Therefore, accurately forecasting the electricity demand for EVs is crucial. This paper presents a hybrid deep learning model for EV charging load prediction based on Multivariate Variational Mode Decomposition (MVMD), Improved Artemisinin Optimization algorithm (IAO), and Deep Representation Learning Extreme Learning Machines (DrELMs). Firstly, MVMD decomposes the original data into several modal components. Secondly, IAO optimizes the hyperparameters of the DrELM model. Finally, the trained IAO-DrELM model predicts multiple modal components following MVMD decomposition to obtain the final predictions. Experimental results show that the proposed model outperforms eight other models, achieving the lowest Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE) error values and the highest Coefficient of Determination (R2) value. Full article
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