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The role of data analysis in the development of intelligent energy networks
Beijing University of Posts and Telecommunications, China.
Beijing University of Posts and Telecommunications, China.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center. Tianjin University of Commerce, China.ORCID iD: 0000-0002-6279-4446
Shandong University, China.
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2017 (English)In: IEEE Network, ISSN 0890-8044, E-ISSN 1558-156X, Vol. 31, no 5, 88-95 p., 8053484Article in journal (Refereed) Published
Abstract [en]

Data analysis plays an important role in the development of intelligent energy networks (IENs). This article reviews and discusses the application of data analysis methods for energy big data. The installation of smart energy meters has provided a huge volume of data at different time resolutions, suggesting data analysis is required for clustering, demand forecasting, energy generation optimization, energy pricing, monitoring and diagnostics. The currently adopted data analysis technologies for IENs include pattern recognition, machine learning, data mining, statistics methods, and so on. However, existing methods for data analysis cannot fully meet the requirements for processing the big data produced by IENs, therefore more comprehensive data analysis methods are needed to handle the increasing amount of data and to mine more valuable information.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2017. Vol. 31, no 5, 88-95 p., 8053484
Keyword [en]
Data handling, Data mining, Information analysis, Learning systems, Pattern recognition, Data analysis methods, Demand forecasting, Energy generations, Intelligent energies, Monitoring and diagnostics, Smart energy meters, Statistics method, Time resolution, Big data
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-37154DOI: 10.1109/MNET.2017.1600319Scopus ID: 2-s2.0-85031325051OAI: oai:DiVA.org:mdh-37154DiVA: diva2:1152762
Available from: 2017-10-26 Created: 2017-10-26 Last updated: 2017-10-26Bibliographically approved

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Li, Hailong

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