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Computational awareness for smart grid: a review

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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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Notes

  1. The smart meter system usually includes smart meter, communication infrastructure, and control devices.

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Acknowledgments

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.

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  1. Department of Applied Informatics and Multimedia, Chia Nan University of Pharmacy and Science, Tainan, Taiwan, ROC

    Chun-Wei Tsai

  2. Department of Networks, Security and Multimedia, IMT/TELECOM Bretagne/IRISA, Cesson-Sévigné, France

    Alexander Pelov

  3. Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan, ROC

    Ming-Chao Chiang

  4. Department of Electrical Engineering, National Cheng Kung University, Tainan, Taiwan, ROC

    Chu-Sing Yang

  5. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan, ROC

    Tzung-Pei Hong

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Tsai, CW., Pelov, A., Chiang, MC. et al. Computational awareness for smart grid: a review. Int. J. Mach. Learn. & Cyber. 5, 151–163 (2014). https://doi.org/10.1007/s13042-013-0185-1

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