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Non-intrusive Load Monitoring Algorithms for Privacy Mining in Smart Grid

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Advances in Cyber Security: Principles, Techniques, and Applications

Abstract

Non-intrusive load monitoring (NILM) method is essentially artificial intelligence algorithms for energy conservation and privacy mining. It obtains consumers’ privacy data by decomposing aggregated meter readings of consumer energy consumption into the individual devices energy consumption. In this chapter, we first introduce the background and the advantage of the NILM method, and the classification of NILM method. Secondly, we demonstrate the general process of NILM method. The specific process contains data preprocess, event detection and feature extraction, and energy consumption learning and appliance inference. Furthermore, we introduce a supervised NILM example and an unsupervised example. We describe their processes, and discuss their characteristics and performances. In addition, the applications of NILM method are depicted. Lastly, we conclude this chapter and give the future work.

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Authors and Affiliations

  1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, People’s Republic of China

    Zijian Zhang, Jialing He & Liehuang Zhu

  2. Institute of Cyber Security Research and School of Computer Science and Engineering, Zhejiang University, Hangzhou, 310058, China

    Kui Ren

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  1. Zijian Zhang
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  3. Liehuang Zhu
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  4. Kui Ren
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Correspondence to Jialing He .

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Editors and Affiliations

  1. Department of Computer Science and Information Engineering, Providence University, Taichung, Taiwan

    Kuan-Ching Li

  2. School of Cyber Engineering, Xidian University, Xi'an, Shaanxi, China

    Xiaofeng Chen

  3. School of Computing and Information Technology, University of Wollongong, Wollongong, NSW, Australia

    Willy Susilo

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Zhang, Z., He, J., Zhu, L., Ren, K. (2019). Non-intrusive Load Monitoring Algorithms for Privacy Mining in Smart Grid. In: Li, KC., Chen, X., Susilo, W. (eds) Advances in Cyber Security: Principles, Techniques, and Applications. Springer, Singapore. https://doi.org/10.1007/978-981-13-1483-4_2

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  • DOI: https://doi.org/10.1007/978-981-13-1483-4_2

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