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Studies from Shanghai University Further Understanding of Smart Grids (Clustering Load Profiles for Demand Response Applications)

Source: 
Energy Daily News

2019 MAR 20 (NewsRx) -- By a News Reporter-Staff News Editor at Energy Daily News -- Investigators publish new report on Energy - Smart Grids. According to news originating from Shanghai, People’s Republic of China, by NewsRx correspondents, research stated, “With the development of smart grid technologies, residential and commercial loads have large potentialities to participate in demand response (DR) programs. This makes the data dimension reduction techniques and classification processing critical for the success of DR development.”

Financial supporters for this research include National Natural Science Foundation of China, Science and Technology Commission of Shanghai, Shanghai Green Energy Grid Connected Technology Engineering Research Center, Shanghai Municipal Education Commission.

Our news journalists obtained a quote from the research from Shanghai University, “A novel load profile clustering method is proposed for load data classification which is based on the information entropy, piecewise aggregate approximation, and spectral clustering (SC). The variable temporal resolution technique is presented to model typical daily load datasets, and then an improved SC based on multi-scale similarities of distance and shape characteristics is proposed for clustering to obtain reasonable load classification. A case study with one hundred of commercial heating, ventilation, and air conditioning data analysis illustrates the approach.”

According to the news editors, the research concluded: “The results prove that the proposed method is feasible in terms of data dimension reduction, reasonable profile selection and classification, and the operation stability.”

For more information on this research see: Clustering Load Profiles for Demand Response Applications. IEEE Transactions on Smart Grid, 2019;10(2):1599-1607. IEEE Transactions on Smart Grid can be contacted at: Ieee-Inst Electrical Electronics Engineers Inc, 445 Hoes Lane, Piscataway, NJ 08855-4141, USA. (Institute of Electrical and Electronics Engineers - http://www.ieee.org/; IEEE Transactions on Smart Grid - http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5165411)

The news correspondents report that additional information may be obtained from Y. Fu, Shanghai Univ Elect Power, Coll Elect Engn, Shanghai 200090, People’s Republic of China. Additional authors for this research include S.F. Lin, D.D. Li, F.X. Li and E.W. Tian.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1109/TSG.2017.2773573. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

 

(Our reports deliver fact-based news of research and discoveries from around the world.)

 

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