Load Forecasting for Charging Station Weather

Le résumé fourni par la source

This paper addresses the issue of “time shift” in electric vehicle charging load caused by the influence of meteorological factors, proposing a load forecasting method tailored to meteorology at charging stations. The charging pile and meteorological datasets from Chongqing are processed and normalized. Two load forecasting models, RF Random Forest and BIGRU algorithm, are constructed, with the inclusion of an attention mechanism to enhance prediction accuracy. Experimental results demonstrate the superior performance of the BIGRU-Attention model, particularly when considering key meteorological factors. This method effectively addresses the temporal characteristics of charging load and the influence of meteorological factors, providing reliable load forecasting support for practical grid operation.

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