Transferability of neural networks approaches for low-rate energy disaggregation

Murray, David and Stankovic, Lina and Stankovic, Vladimir and Lulic, Srdjan and Sladojevic, Srdjan (2019) Transferability of neural networks approaches for low-rate energy disaggregation. In: 2019 International Conference on Acoustics, Speech, and Signal Processing, 2019-05-12 - 2019-05-17. (In Press)

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Abstract

Energy disaggregation of appliances using non-intrusive load monitoring (NILM) represents a set of signal and information processing methods used for appliance-level information extraction out of a meter's total or aggregate load. Large-scale deployments of smart meters worldwide and the availability of large amounts of data, motivates the shift from traditional source separation and Hidden Markov Model-based NILM towards data-driven NILM methods. Furthermore, we address the potential for scalable NILM roll-out by tackling disaggregation complexity as well as disaggregation on houses which have not been 'seen' before by the network, e.g., during training. In this paper, we focus on low rate NILM (with active power meter measurements sampled between 1-60 seconds) and present two different neural network architectures, one, based on convolutional neural network, and another based on gated recurrent unit, both of which classify the state and estimate the average power consumption of targeted appliances. Our proposed designs are driven by the need to have a well-trained generalised network which would be able to produce accurate results on a house that is not present in the training set, i.e., transferability. Performance results of the designed networks show excellent generalization ability and improvement compared to the state of the art.

  • Item type:Conference or Workshop Item(Paper)
    ID code:66112
    Dates:
    Date
    Event
    1 February 2019
    Published
    1 February 2019
    Accepted
    29 October 2018
    Submitted
    Notes:© 2018 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 Engineering > Electronic and Electrical Engineering
    Depositing user: Pure Administrator
    Date deposited:15 Nov 2018 09:43
    Last modified:09 Apr 2024 06:34
    URI:https://strathprints.strath.ac.uk/id/eprint/66112
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