Open AccessSocial Information Filtering-Based Electricity Retail Plan Recommender System for Smart Grid End Users
Author(s) -
Fengji Luo,
Gianluca Ranzi,
Xibin Wang,
Zhao Yang Dong
Publication year - 2017
Publication title -
ieee transactions on smart grid
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.571
H-Index - 171
eISSN - 1949-3061
pISSN - 1949-3053
DOI - 10.1109/tsg.2017.2732346
Subject(s) - smart grid , recommender system , smart meter , computer science , electricity , energy consumption , demand response , end user , grid , user information , consumption (sociology) , big data , plan (archaeology) , service (business) , collaborative filtering , key (lock) , analytics , database , data mining , information system , world wide web , computer security , engineering , mathematics , social science , economy , history , archaeology , sociology , geometry , electrical engineering , economics
Rapid growth of data in smart grids provides great potentials for the utility to discover knowledge of demand side and design proper demand side management schemes to optimize the grid operation. The overloaded data also impose challenges on the data analytics and decision making. This paper introduces the service computing technique into the smart grid, and proposes a personalized electricity ret...
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