A low-complexity energy disaggregation method : performance and robustness

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Altrabalsi, Hana and Liao, Jing and Stankovic, Lina and Stankovic, Vladimir; (2014) A low-complexity energy disaggregation method : performance and robustness. In: 2014 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG). IEEE, USA, pp. 1-8. (https://doi.org/10.1109/CIASG.2014.7011569)

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Abstract

Disaggregating total household's energy data down to individual appliances via non-intrusive appliance load monitoring (NALM) has generated renewed interest with ongoing or planned large-scale smart meter deployments worldwide. Of special interest are NALM algorithms that are of low complexity and operate in near real time, supporting emerging applications such as in-home displays, remote appliance scheduling and home automation, and use low sampling rates data from commercial smart meters. NALM methods, based on Hidden Markov Model (HMM) and its variations, have become the state of the art due to their high performance, but suffer from high computational cost. In this paper, we develop an alternative approach based on support vector machine (SVM) and k-means, where k-means is used to reduce the SVM training set size by identifying only the representative subset of the original dataset for the SVM training. The resulting scheme outperforms individual k-means and SVM classifiers and shows competitive performance to the state-of-the-art HMM-based NALM method with up to 45 times lower execution time (including training and testing).

ORCID iDs

Altrabalsi, Hana ORCID logoORCID: https://orcid.org/0000-0002-0605-0151, Liao, Jing ORCID logoORCID: https://orcid.org/0000-0002-5614-7939, Stankovic, Lina ORCID logoORCID: https://orcid.org/0000-0002-8112-1976 and Stankovic, Vladimir ORCID logoORCID: https://orcid.org/0000-0002-1075-2420;
  • Item type:Book Section
    ID code:51404
    Dates:
    Date
    Event
    9 December 2014
    Published
    Notes:(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:02 Feb 2015 13:44
    Last modified:13 Dec 2024 01:02
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/51404
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