SHORT-TERM CHARGING LOAD PREDICTION OF REGIONAL ELECTRIC VEHICLES BASED ON GLDSC-CONVAUTOFORMER MODEL

Li Lianbing; Guo Xingchen; Zeng Siming; Liang Jifeng

doi:10.19912/j.0254-0096.tynxb.2023-1601

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (2) : 90-98. DOI: 10.19912/j.0254-0096.tynxb.2023-1601

SHORT-TERM CHARGING LOAD PREDICTION OF REGIONAL ELECTRIC VEHICLES BASED ON GLDSC-CONVAUTOFORMER MODEL

Li Lianbing¹, Guo Xingchen², Zeng Siming³, Liang Jifeng³

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Abstract

Aiming at the problem of load fluctuation caused by large-scale electric vehicles in the grid-connected process, short-term load prediction of electric vehicles provides decision-making basis for optimal scheduling of electric vehicles. In order to better ensure the stability and reliability of the power grid, a short-term charging load prediction method of electric vehicles is proposed to improve the load prediction accuracy. Firstly, according to the spatiotemporal differences of EV charging on each charging pile, a grey relational degree model based on dynamic time warping under limited warping path length algorithm is constructed. The correlation degree matrix was used as the degree matrix of spectral clustering algorithm, and the gray limited dynamic spectrum clustering algorithm model was constructed. Cluster the daily charging load curves of all electric vehicles to make the clustering data have better periodicity. Secondly, the cluster data were processed by double convolution, and the extracted data features were input into Autoformer respectively to build ConvAutoformer load prediction model, and load prediction was carried out on the cluster results respectively. Finally, the actual charging load data of electric vehicle charging pile was used for example analysis. Experimental results show that the proposed method can effectively improve the accuracy of short-term charging load predict

Key words

electric vehicles / feature extraction / forecasting / limited dynamic time bending distance / grey limited dynamic spectrum clustering / ConvAutoformer

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Li Lianbing, Guo Xingchen, Zeng Siming, Liang Jifeng. SHORT-TERM CHARGING LOAD PREDICTION OF REGIONAL ELECTRIC VEHICLES BASED ON GLDSC-CONVAUTOFORMER MODEL[J]. Acta Energiae Solaris Sinica. 2025, 46(2): 90-98 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1601

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