ISSN 1000-1026
CN 32-1180/TP
  • ISSN 1000-1026
  • CN 32-1180/TP

Citation: CHENG Lefeng,YU Tao,ZHANG Xiaoshun,YIN Linfei.Machine Learning for Energy and Electric Power Systems: State of the Art and Prospects[J].Automation of Electric Power Systems,2019,43(1):15-31. DOI: 10.7500/AEPS20180814007 copy

Machine Learning for Energy and Electric Power Systems: State of the Art and Prospects

  • Received Date: August 14, 2018
    Accepted Date: November 12, 2018
    Available Online: November 28, 2018

  • Abstract:

        The new generation of artificial intelligence(AI), i. e. , AI2. 0, has become a research highlight in recent years. Among AI2. 0, machine learning(ML)as a typical representative is an algorithm category that completes predictions and judgments for optimal decision-making through analyzing and learning a large amount of existing or generated data. AI2. 0 is developing rapidly in China, and it has been preliminarily applied to the energy and electric power system(EEPS)that contains smart grid(SG)and energy interconnection(EI)fields. To this end, this paper takes ML in AI2. 0 as an example to review the current application of seven representative MLs in EEPS from aspects of dispatch optimization and control decision-making, including reinforcement learning, deep learning, transfer learning, parallel learning, hybrid learning, adversarial learning, and ensemble learning. Finally, the prospects for the future development of ML are conducted, trying to provide some reference for the theoretical, technical and application studies of AI2. 0, especially ML in the field of EEPS in the future.


  • Keywords:

    artificial intelligence(AI); machine learning; energy and electric power system(EEPS); smart grid; energy interconnection


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