A generic optimisation-based approach for improving non-intrusive load monitoring

He, Kanghang and Jakovetic, Dusan and Zhao, Bochao and Stankovic, Vladimir and Stankovic, Lina and Cheng, Samuel (2019) A generic optimisation-based approach for improving non-intrusive load monitoring. IEEE Transactions on Smart Grid, 10 (6). pp. 6472-6480. ISSN 1949-3053 (https://doi.org/10.1109/TSG.2019.2906012)

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Abstract

The large-scale deployment of smart metering worldwide has ignited renewed interest in electrical load disaggregation, or non-intrusive load monitoring (NILM). Most NILM algorithms disaggregate one appliance at a time, remove the estimated appliance contribution from the total load, and then move on to disaggregate the next appliance. On one hand, this is efficient since multi-class classification is avoided and analytical models for each appliance can be developed independently of other appliances with the benefit of being transferred to unseen houses that have different sets of appliances. On the other hand, however, these methods can significantly under- or over- estimate the total consumption since they do not minimise the difference between the measured aggregate load and the sum of estimated individual loads. Motivated by minimising the latter difference without losing the benefits of existing NILM algorithms, we propose novel post-processing approaches for improving the accuracy of existing NILM. This is posed as an optimisation problem to refine the final NILM result using regularisation, based on the level of confidence in the original NILM output. First, we propose a heuristic method to solve this (combinatorial) boolean quadratic problem through relaxing zero-one constraint sets to compact zero-one intervals. Convex-based solutions, including norm-1, norm-2 and semi-definite programming-based relaxation, are proposed trading off accuracy and complexity. We demonstrate good performance of the proposed post-processing methods, applicable to any event-based NILM, compared with 4 state-of-the-art benchmarks, using public REFIT and REDD electrical load datasets.

  • Item type:Article
    ID code:67341
    Dates:
    Date
    Event
    1 November 2019
    Published
    19 March 2019
    Published Online
    13 March 2019
    Accepted
    Notes:© 2019 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:18 Mar 2019 14:36
    Last modified:25 Apr 2024 01:02
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/67341
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