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Monitoring Home Energy Usage Using an Unsupervised NILM Algorithm Based on Entropy Index Constraints Competitive Agglomeration (EICCA)

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Cloud Computing and Security (ICCCS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11067))

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Abstract

Given that residential sectors in both developed and developing nations contribute to a significant portion of electric energy consumption, addressing energy efficiency and conservation in this sector is envisioned to have a considerable effect on the levels of nationwide and global electric energy consumption. Various approaches have been utilized to address these challenges with a number of positive outcomes being realized through Load Monitoring and Non-Intrusive Load Monitoring (NILM) in particular. These positive outcomes have been attributed to the increase in energy awareness of homeowners. Due to limited resources in a residential environment, methods utilizing unsupervised learning together with NILM can provide valuable and practical solutions. Such solutions are of great importance to developing nations and low-income households as they lower the barrier for adoption by reducing the costs and effort required to monitor electric energy usage. In this paper we present a low-complexity unsupervised NILM algorithm which has practical applications for monitoring electric energy usage within homes. We make use of Entropy Index Constraints Competitive Agglomeration (EICCA) to automatically discover an optimal set of feature clusters, and invariant Active Power (P) features to detect appliance usage given aggregated household energy data which includes noise. We further present an approach that can be used to obtain Type II appliance models, which can provide valuable feedback to homeowners. The results of experimental validation indicate that our proposed work has comparable performance with recent work in unsupervised NILM including the state of the art with regards to energy disaggregation.

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Acknowledgements

This work has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 701697, Major Program of the National Social Science Fund of China (Grant No. 17ZDA092) and the PAPD fund.

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Authors and Affiliations

  1. Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, China

    Kondwani M. Kamoto

  2. School of Computing, Edinburgh Napier University, 10 Colinton Road, Edinburgh, EH10 5DT, UK

    Qi Liu

Authors
  1. Kondwani M. Kamoto
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  2. Qi Liu
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Correspondence to Qi Liu .

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Zhaoqing Pan

  3. Department of Computer Science, Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Kamoto, K.M., Liu, Q. (2018). Monitoring Home Energy Usage Using an Unsupervised NILM Algorithm Based on Entropy Index Constraints Competitive Agglomeration (EICCA). In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11067. Springer, Cham. https://doi.org/10.1007/978-3-030-00018-9_42

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  • DOI: https://doi.org/10.1007/978-3-030-00018-9_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00017-2

  • Online ISBN: 978-3-030-00018-9

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