A Brief Introduction to Classification for Smart Grid

Chun-Wei Tsai (1) , Alexander Pelov (2, 3) , Ming-Chao Chiang (4) , Chu-Sing Yang (5) , Tzung-Pei Hong (6)

1 Chia Nan University of Pharmacy & Science
2 RSM - Département Réseaux, Sécurité et Multimédia
3 OCIF - Objets communicants pour l'Internet du futur
4 SYSU - Sun Yat-sen University [Guangzhou]
5 NCKU - National Cheng Kung University
6 NUK - National University of Kaohsiung

There is no doubt about the potentials of smart grid, for the traditional power grid has been kind of out of date, in terms of not only its infrastructure but also its restrictions on the way information is communicated. To provide better and smarter services via smart grid, the first thing we have to do is to make it more intelligent. Among the technologies that can be applied to smart grid to make it more intelligent, data mining will certainly play a vital role. This paper begins with a brief introduction to smart grid, followed by a discussion on the supervised learning (classification) and the unsupervised learning (clustering). Several open and possible research issues are then given to depict the future trends of smart grid.

Mots clés: Smart grid; Data mining; Machine learning

Domaines: Réseaux et télécommunications [cs.NI]

Chun-Wei Tsai, Alexander Pelov, Ming-Chao Chiang, Chu-Sing Yang, Tzung-Pei Hong. A Brief Introduction to Classification for Smart Grid. SMC 2013 : IEEE International Conference on Systems, Man, and Cybernetics, Oct 2013, Manchester, United Kingdom. pp.2905-2909, ⟨10.1109/SMC.2013.495⟩. ⟨hal-00958308⟩