Journal of Shanghai Jiao Tong University ›› 2022, Vol. 56 ›› Issue (8): 1004-1013.doi: 10.16183/j.cnki.jsjtu.2021.486

• New Type Power System and the Integrated Energy • Previous Articles     Next Articles

Ultra-Short-Term Load Forecasting of Electric Vehicle Charging Stations Based on Ensemble Learning

LI Hengjie1,3, ZHU Jianghao1, FU Xiaofei2, FANG Chen2, LIANG Daming1, ZHOU Yun3()   

  1. 1. School of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China
    2. State Grid Shanghai Municipal Electric Power Company, Shanghai 200122, China
    3. Key Laboratory of Power Transmission and Power Conversion Control of the Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China
  • Received:2021-11-30 Online:2022-08-28 Published:2022-08-26
  • Contact: ZHOU Yun


Accurate electric vehicle load forecasting is the basis for maintaining the safe and economical operation of charging stations, and for supporting the planning and decision-making of new and expanded charging infrastructure. In order to improve the accuracy of the ultra-short-term load forecasting of charging stations, an ultra-short-term load forecasting method based on ensemble learning is proposed. First, aimed at the prediction accuracy and the response speed, the light gradient boosting machine (LightGBM) framework is utilized to build several basic regressors. Next, the basic regressors are integrated by using the adaptive boosting (Adaboost) method. Finally, by using hyperparameter adjustment and optimization, a dual-system for ultra-short-term load forecasting of charging stations named energy ensemble boosting-light gradient boosting machine (EEB-LGBM) is generated. The analysis of the numerical examples shows that the proposed model has a higher accuracy than the back propagation neural network (BPNN), convolutional neural networks-long short term memory (CNN-LSTM), autoregressive integrated moving average (ARIMA), and other load forecasting methods, which can greatly reduce the training time and the computing power requirements of the training platform.

Key words: electric vehicle charging station, charging load, ultra-short-term forecasting, ensemble learning, economy

CLC Number: