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An Efficient Sparse Coding-Based Data-Mining Scheme in Smart Grid

  • Dongshu Wang
  • Jialing He
  • Mussadiq Abdul Rahim
  • Zijian Zhang
  • Liehuang Zhu
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 747)

Abstract

With the availability of Smart Grid, disaggregation, i.e. decomposing a whole electricity signal into its component appliances has gotten more and more attentions. Now the solutions based on the sparse coding, i.e. the supervised learning algorithm that belongs to Non-Intrusive Load Monitoring (NILM) have developed a lot. But the accuracy and efficiency of these solutions are not very high, we propose a new efficient sparse coding-based data-mining (ESCD) scheme in this paper to achieve higher accuracy and efficiency. First, we propose a new clustering algorithm – Probability Based Double Clustering (PDBC) based on Fast Search and Find of Density Peaks Clustering (FSFDP) algorithm, which can cluster the device consumption features fast and efficiently. Second, we propose a feature matching optimization algorithm – Max-Min Pruning Matching (MMPM) algorithm which can make the feature matching process to be real-time. Third, real experiments on a publicly available energy data set REDD [1] demonstrate that our proposed scheme achieves a for energy disaggregation. The average disaggregation accuracy reaches 77% and the disaggregation time for every 20 data is about 10 s.

Keywords

Smart Grid Energy disaggregation Sparse coding Data mining 

Notes

Acknowledgment

This work is partially supported by China National Key Research and Development Program No. 2016YFB0800301.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Dongshu Wang
    • 1
  • Jialing He
    • 1
  • Mussadiq Abdul Rahim
    • 1
  • Zijian Zhang
    • 1
  • Liehuang Zhu
    • 1
  1. 1.Beijing Institute of TechnologyBeijingChina

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