Path signatures for non-intrusive load monitoring

Moore, Paul and Iliant, Theodor-Mihai and Ion, Filip-Alexandru and Wu, Yue and Lyons, Terry; (2022) Path signatures for non-intrusive load monitoring. In: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, SGP, pp. 3808-3812. ISBN 9781665405409 (https://doi.org/10.1109/ICASSP43922.2022.9747285)

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Abstract

Non-intrusive load monitoring (NILM) is the analysis of electricity loads by means of a single supply wire, so avoiding separate monitors on individual appliances. Some approaches to NILM use the V-I trajectory for feature generation but they apply ad-hoc rules to generate the feature vector. This paper demonstrates a systematic method of feature generation called the path signature which has recently been applied in machine learning, often with notable success. We show how the path signature generates features from the V-I trajectory to give a test set accuracy of 98.81% on the COOLL dataset. We conclude that the path signature is easier to use and generalize than ad-hoc features, and it can be applied to many other applications which use multivariate sequential data.

  • Item type:Book Section
    ID code:80812
    Dates:
    Date
    Event
    27 May 2022
    Published
    27 April 2022
    Published Online
    21 January 2022
    Accepted
    Notes:© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works.
    Subjects:Technology > Electrical engineering. Electronics Nuclear engineering
    Department:Faculty of Science > Mathematics and Statistics
    Depositing user: Pure Administrator
    Date deposited:19 May 2022 09:25
    Last modified:11 Nov 2024 15:29
    URI:https://strathprints.strath.ac.uk/id/eprint/80812
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