The growing number of electric vehicles (EVs) has brought new changes to residential electricity consumption patterns. Therefore, predicting EV charging demand accurately is important for the operation and management of distribution networks. This paper presents a forecasting method that combines a Convolutional Neural...
Yi-Xin Zhang, Jian Ai, Tian-Qi Liu et al.· 2026 6th Power System and Gr...· 0 citations
Multi-scale graph neural networks have shown strong performance in multivariate time-series forecasting by integrating representations at different temporal resolutions. However, existing fusion mechanisms typically assign the same scale weights to all nodes, implicitly assuming spatially homogeneous temporal preferenc...
The proposed method remains more accurate at 15 and 10 dB, including under 88% phase-current unbalance, confirming MCU-executable diagnosis under a lagging power-factor load for embedded EV inverter protection.
Mingxing Fang, Wen-Xu Yan, Wen-Yuan Wang· World Electric Vehicle Journ...· 0 citations
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