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

Zhe WANG; Bao WAN; Tianhan LING; Xiaohong DONG; Yunfei MU; Youjun DENG; Shuyi TANG

doi:10.12204/j.issn.1000-7229.2021.06.006

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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?

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

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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

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Zhe WANG , Bao WAN , Tianhan LING , et al . Electric Bus Charging Load Forecasting Method Based on Spectral Clustering and LSTM Neural Network[J]. Electric Power Construction. 2021, 42(6): 58-66 https://doi.org/10.12204/j.issn.1000-7229.2021.06.006

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