• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

ELECTRIC POWER CONSTRUCTION ›› 2021, Vol. 42 ›› Issue (6): 58-66.doi: 10.12204/j.issn.1000-7229.2021.06.006

• Key Technologies of Electric Vehicle Participating in Power Grid Dispatching?Hosed by Associate Professor FU Zhixin? • Previous Articles     Next Articles

Electric Bus Charging Load Forecasting Method Based on Spectral Clustering and LSTM Neural Network

WANG Zhe1, WAN Bao2, LING Tianhan2, DONG Xiaohong3, MU Yunfei3, DENG Youjun3, TANG Shuyi3   

  1. 1. State Grid Tianjin Electric Power Company, Tianjin 300010, China
    2. State Grid Tianjin Binhai Electric Power Company, Tianjin 300450, China
    3. Key Laboratory of Smart Grid of Ministry of Education (Tianjin University), Tianjin 300072, China
  • Received:2020-10-20 Online:2021-06-01 Published:2021-05-28
  • Supported by:
    Science and Technology Program of State Grid Tianjin Electric Power Company(KJ20-1-38)

Abstract:

At present, the penetration rate, charging frequency and charging capacity of electric buses are relatively high, so the charging load has a non-negligible impact on the operation and dispatch of the power grid. So, the charging load forecasting research has important theoretical and practical significance, but the intermittent and random charging behavior increase the spatial forecasting difficulty. Therefore, the charging load forecasting method of electric buses is proposed on the basis of spectral clustering and long short-term memory (LSTM) neural network. First of all, the charging load curve is clustered according to spectral clustering considering the distance and the shape. And then, considering the key factors that affect the charging load, such as historical load, temperature and day type, the model parameter of LSTM neural network is trained using each cluster charging load, and the charging load of each cluster is predicted. Then, the total charging load of the forecasting day is to sum the forecasting results of different clusters. Finally, on the basis of the historical real data in a certain city, the proposed method is verified. The result shows the mean absolute percentage error (MAPE) of charging load prediction result of the proposed method is below 11%, and the accuracy of load forecasting is improved.

Key words: spectral clustering, long short-term memory network (LSTM), electric bus, load forecasting

CLC Number: