Computer Engineering and Applications ›› 2021, Vol. 57 ›› Issue (18): 275-280.DOI: 10.3778/j.issn.1002-8331.2012-0135

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Short-Term Power Load Forecasting Using Cascaded LSTM Models

LOU Qihe, LIU Hu, XIE Xiangying, MA Xiaoguang   

  1. 1.North China Electric Power University, Beijing 102206, China
    2.State Grid Corporation of China, Beijing 100031, China
    3.State Grid E-commerce Co., Ltd., Beijing 100053, China
  • Online:2021-09-15 Published:2021-09-13



  1. 1.华北电力大学,北京 102206
    2.国家电网有限公司,北京 100031
    3.国网电子商务有限公司,北京 100053


With the rapid development of clean energy in China, distributed photovoltaic power station has been vigorously promoted. In the operation and maintenance of photovoltaic power system, power load forecasting is the key factor that affects the optimal configuration of distributed photovoltaic power station, such as power generation, energy storage, transmission and so on. Aiming at the problem of power load forecasting, this paper proposes a cascaded long-short-term-memory model, which divides power load forecasting into two stages. In the first stage, the periodic characteristics of power load are extracted to get the overall trend. In the second stage, the fluctuation characteristics of load are extracted to modify the overall trend to further improve the accuracy of prediction. In this paper, the experimental verification is carried out on a five-year continuous power load data set, and the results show that the cascaded LSTM model can greatly reduce the prediction error. The model can provide more accurate load forecasting for distributed photovoltaic power plants, which can provide important support for its intelligent operation and maintenance services.

Key words: power load forecasting, long-short-term-memory network, photovoltaic power plants, artificial intelligence



关键词: 电力负荷预测, 长短期记忆网络, 光伏电站, 人工智能