文章摘要
程乐峰,余涛,张孝顺,等.机器学习在能源与电力系统领域的应用和展望[J].电力系统自动化,2019,43(1):15-31. DOI: 10.7500/AEPS20180814007.
CHENG Lefeng,YU Tao,ZHANG Xiaoshun, et al.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.
机器学习在能源与电力系统领域的应用和展望
Machine Learning for Energy and Electric Power Systems: State of the Art and Prospects
DOI:10.7500/AEPS20180814007
关键词: 人工智能  机器学习  能源与电力系统  智能电网  能源互联网
KeyWords: artificial intelligence(AI)  machine learning  energy and electric power system(EEPS)  smart grid  energy interconnection
上网日期:2018-11-28
基金项目:国家自然科学基金资助项目(51477055,51777078);中国南方电网有限责任公司重点科技项目(GZKJQQ00000419)
作者单位E-mail
程乐峰 华南理工大学电力学院, 广东省广州市 510641
广东省绿色能源技术重点实验室, 广东省广州市 510641 
 
余涛 华南理工大学电力学院, 广东省广州市 510641
广东省绿色能源技术重点实验室, 广东省广州市 510641 
taoyu1@scut.edu.cn 
张孝顺 汕头大学工学院, 广东省汕头市 515063  
殷林飞 广西大学电气工程学院, 广西壮族自治区南宁市 530004  
摘要:
      新一代人工智能(AI)近年来成为国内外研究的热点,其中的典型代表机器学习(ML)作为一个算法范畴,通过分析和学习大量已有或生成数据形成预测和判断以做出最佳决策。中国的新一代AI正处于快速发展的关键期,目前已在能源与电力系统中得到初步应用。基于此,文中以新一代AI中的ML为代表,重点综述了强化学习、深度学习、迁移学习、平行学习、混合学习、对抗学习和集成学习等7种代表性ML在能源与电力系统调度优化和控制决策等方面的应用。最后,对未来ML的发展进行了思考与展望。
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.
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