TY - JOUR
T1 - Computational awareness for smart grid
T2 - A review
AU - Tsai, Chun Wei
AU - Pelov, Alexander
AU - Chiang, Ming Chao
AU - Yang, Chu Sing
AU - Hong, Tzung Pei
N1 - Funding Information:
The authors would like to thank the editors and anonymous reviewers for their valuable comments and suggestions on the paper. This work was supported in part by the National Science Council of Taiwan, ROC, under Contracts NSC101-2221-E-041-012, NSC102-2219-E-006-001, and NSC99-2221-E-110-052 and in part by the Ministry of Education of Taiwan, ROC, under Contract D102-23015.
PY - 2014/1
Y1 - 2014/1
N2 - 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.
AB - 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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U2 - 10.1007/s13042-013-0185-1
DO - 10.1007/s13042-013-0185-1
M3 - Review article
AN - SCOPUS:84935012109
SN - 1868-8071
VL - 5
SP - 151
EP - 163
JO - International Journal of Machine Learning and Cybernetics
JF - International Journal of Machine Learning and Cybernetics
IS - 1
ER -