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

  • Zijian Zhang
  • Jialing He
  • Liehuang Zhu
  • Kui Ren
Chapter

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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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Zijian Zhang
    • 1
  • Jialing He
    • 1
  • Liehuang Zhu
    • 1
  • Kui Ren
    • 2
  1. 1.School of Computer Science and TechnologyBeijing Institute of TechnologyBeijingPeople’s Republic of China
  2. 2.Institute of Cyber Security Research and School of Computer Science and EngineeringZhejiang UniversityHangzhouChina

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