Load forecasting for charging stations considering multiple influencing factors and error correction

Abstract

The rapid development of electric vehicles has led to a yearly increase in charging load levels， characterized 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 forecasting. 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 randomness 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 exhibits higher forecasting accuracy and practical value.

Keywords

• electric vehicle
• charging load
• charging station
• load forecasting
• cnn-lstm