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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 1/2014

01-02-2014 | Original Article

Computational awareness for smart grid: a review

Authors: Chun-Wei Tsai, Alexander Pelov, Ming-Chao Chiang, Chu-Sing Yang, Tzung-Pei Hong

Published in: International Journal of Machine Learning and Cybernetics | Issue 1/2014

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Abstract

Smart grid has been an active research area in recent years because almost all the technologies required to build smart grid are mature enough. It is expected that not only can smart grid reduce electricity consumption, but it can also provide a more reliable and versatile service than the traditional power grid can. Although the infrastructure of smart grid all over the world is far from complete yet, there is no doubt that our daily life will benefit a lot from smart grid. Hence, many researches are aimed to point out the challenges and needs of future smart grid. The question is, how do we use the massive data captured by smart meters to provide services that are as “smart” as possible instead of just automatically reading information from the meters. This paper begins with a discussion of the smart grid before we move on to the basic computational awareness for smart grid. A brief review of data mining and machine learning technologies for smart grid, which are often used for computational awareness, is then given to further explain their potentials. Finally, challenges, potentials, open issues, and future trends of smart grid are addressed.
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Footnotes
1
The smart meter system usually includes smart meter, communication infrastructure, and control devices.
 
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Metadata
Title
Computational awareness for smart grid: a review
Authors
Chun-Wei Tsai
Alexander Pelov
Ming-Chao Chiang
Chu-Sing Yang
Tzung-Pei Hong
Publication date
01-02-2014
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 1/2014
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-013-0185-1

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