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面向能源系统的数据科学:理论、技术与展望
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(1. 香港中文大学(深圳)理工学院, 广东省深圳市 518100; 2. School of Electrical and Information Engineering, The University of Sydney, NSW 2006, 澳大利亚; 3. 南方电网科学研究院, 广东省广州市 510080; 4. 浙江大学电气工程学院, 浙江省杭州市 310027; 5. 文莱科技大学电机与电子工程系, 斯里巴加湾 BE1410, 文莱; 6. 南瑞集团公司(国网电力科学研究院), 江苏省南京市 211106)

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基金项目:

国家重点基础研究发展计划(973计划)资助项目(2013CB228202);国家自然科学基金资助项目(51477151);高等学校博士学科点专项科研基金资助项目(20120101110112)


Data Science for Energy Systems: Theory, Techniques and Prospect
Author:
Affiliation:

(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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    摘要:

    以多能源互补协调、“信息—物理—社会”系统深度融合为特征的大能源系统正在出现。因此,急需对面向能源系统的数据科学及大数据挖掘理论与技术开展深入研究。在此背景下,初步探讨了数据科学及其在大能源系统中的应用。首先介绍了数据科学的基本理论,并着重讨论了统计学习理论及数据质量理论的重要性。接着,介绍了深度学习、转移学习和多源数据融合等大数据挖掘技术的新进展。最后,对数据挖掘技术在能源系统中的应用现状做了简单回顾,并展望了未来能源系统数据挖掘研究中值得关注的若干问题。

    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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引用本文

赵俊华,董朝阳,文福拴,等.面向能源系统的数据科学:理论、技术与展望[J].电力系统自动化,2017,41(4):1-11. DOI:10.7500/AEPS20160813002.
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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  • 收稿日期:2016-08-13
  • 最后修改日期:2016-10-09
  • 录用日期:2016-12-25
  • 在线发布日期: 2017-01-03
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