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面向能源系统的数据科学: 理论、技术与展望

Translated title of the contribution: Data science for energy systems: theory, techniques and prospect
  • 赵俊华
  • , 董朝阳
  • , 文福拴*
  • , 薛禹胜
  • *Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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. © 2017 Automation of Electric Power Systems Press.
Translated title of the contributionData science for energy systems: theory, techniques and prospect
Original languageChinese (Simplified)
Pages (from-to)1-11, 19
Journal电力系统自动化
Volume41
Issue number4
Online published3 Jan 2017
Publication statusPublished - 25 Feb 2017
Externally publishedYes

Research Keywords

  • 大能源系统
  • 智能电网
  • “信息—物理—社会” 系统
  • 数据科学
  • 大数据
  • Comprehensive energy system
  • Smart grid
  • “cyber-physical-social” system
  • Data science
  • Big data

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