The rapid development of electric vehicles has led to a yearly increase in charging load levels,character-ized by strong randomness and unpredictability.Therefore,research on load forecasting for charging stations holds significant importance.Firstly,to address the inaccuracy of single-factor forecasting models that only consider load fluctuation trends,this paper analyzes the impact of multiple factors on the accuracy of charging station load fore-casting.A load forecasting model is established that takes into account multiple influencing factors and is based on CNN-LSTM(convolutional neural network,long short-term memory).Subsequently,given the impact of strong ran-domness of charging load on the model,an error correction method based on the random forest(RF)algorithm is proposed.Finally,the paper conducts simulation verification using real charging station load data as a case study.The research results indicate that the load prediction of the CNN-LSTM model,corrected by the RF algorithm,can accurately cover real values.Compared to the LSTM single model and the non-corrected CNN-LSTM model,it exhib-its higher forecasting accuracy and practical value.
electric vehiclecharging loadcharging stationload forecastingCNN-LSTM
国家科技期刊平台
NSTL
维普
万方数据
