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Data Science for Energy Systems: Theory, Techniques and Prospect
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(1. School of Science and Engineering, Chinese University of Hong Kong(Shenzhen), Shenzhen 518100, China;2. School of Electrical and Information Engineering, The University of Sydney, NSW 2006, Australia;3. China Southern Power Grid Electric Power Research Institute, Guangzhou 510080, China;4. School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China;5. Department of Electrical & Electronic Engineering, Universiti Teknologi Brunei, Bandar Seri Begawan BE1410, Brunei;6. NARI Group Corporation(State Grid Electric Power Research Institute), Nanjing 211106, China)

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    Abstract:

    The comprehensive energy system, which can coordinate multiple types of energy and be characterized by a deep integration of “cyber-physical-social” systems, is emerging. There is therefore an urgent need to conduct in-depth study on data science and big data mining for energy systems. This paper presents an initial discussion on data science and its applications in comprehensive energy systems. The fundamentals of data science, in particular the importance of the statistical learning theory and data quality, are discussed first. The new progresses in big data mining, such as deep learning, transfer learning and cross domain data fusion, are introduced then. Finally, a brief review is given on the applications of data mining techniques in energy systems; some research problems in energy system data mining, which require further attentions in future, are also discussed. This work is supported by National Basic Research Program of China(973 Program)(No. 2013CB228202), National Natural Science Foundation of China(No. 51477151)and Specialized Research Fund for the Doctoral Program of Higher Education of China(No. 20120101110112).

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ZHAO Junhua, DONG Zhaoyang, WEN Fushuan,et al.Data Science for Energy Systems: Theory, Techniques and Prospect[J].Automation of Electric Power Systems,2017,41(4):1-11.DOI:10.7500/AEPS20160813002

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History
  • Received:August 13,2016
  • Revised:October 09,2016
  • Adopted:December 25,2016
  • Online: January 03,2017
  • Published: