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Deep COLA: A Deep COmpetitive Learning Algorithm for Future Home Energy Management Systems

Mohi-Ud-din, G, Marnerides, A, Shi, Q, Dobbins, C and MacDermott, AM (2020) Deep COLA: A Deep COmpetitive Learning Algorithm for Future Home Energy Management Systems. IEEE Transactions on Emerging Topics in Computational Intelligence, 5 (6). pp. 860-870. ISSN 2471-285X

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A smart grid ecosystem requires intelligent Home Energy Management Systems (HEMSs) that allow the adequate monitoring and control of appliance-level energy consumption in a given household. They should be able to: i) profile highly non-stationary and non-linear measurements and ii) conduct correlations of such measurements with diverse inputs (e.g. environmental factors) in order to improve the end-user experience, as well as to aid the overall demand-response optimisation process. However, traditional approaches in HEMS lack the ability to capture diverse variations in appliance-level energy consumption due to unpredictable human behaviour and also require high computation to process large datasets. In this paper, we go beyond current profiling schemes by proposing Deep COLA; a novel Deep COmpetitive Learning Algorithm that addresses the limitations of existing work in terms of high dimensional data and enables more efficient and accurate clustering of appliancelevel energy consumption. The proposed approach reduces human intervention by automatically selecting load profiles and models variations and uncertainty in human behaviour during appliance usage. We demonstrate that our proposed scheme is far more computationally efficient and scalable data-wise than three popular conventional clustering approaches namely, K-Means, DBSCAN and SOM, using real household datasets. Moreover, we exhibit that Deep COLA identifies per-household behavioral associations that could aid future HEMSs.

Item Type: Article
Additional Information: © 2020 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: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > T Technology (General)
T Technology > TD Environmental technology. Sanitary engineering
Divisions: Computer Science & Mathematics
Publisher: Institute of Electrical and Electronics Engineers
Date Deposited: 15 Oct 2020 09:19
Last Modified: 22 Aug 2022 14:00
DOI or ID number: 10.1109/TETCI.2020.3027300
URI: https://researchonline.ljmu.ac.uk/id/eprint/13838
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