Self-learning load characteristic models for smart appliances

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Stephen, Bruce and Galloway, Stuart and Burt, Graeme (2014) Self-learning load characteristic models for smart appliances. IEEE Transactions on Smart Grid, 5 (5). ISSN 1949-3053 (https://doi.org/10.1109/TSG.2014.2318375)

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Abstract

It is generally accepted that if dynamic electricity pricing tariffs were to be introduced, their effectiveness in controlling domestic loads will be curtailed if consumers were relied on to respond in their own interests. The complexities of relating behavior to load to price are so burdensome that at least some degree of automation would be required to take advantage of pricing signals. However, a major issue with home automation is fitting in with the lifestyles of individual consumers. Truly smart appliances that can learn the details of their routine operation may be several years away from widespread adoption making integrated home energy management systems unfeasible. Similarly, usage patterns of these same appliances may be substantially different from household to household. The contribution of this paper is the proposal and demonstration of a set of probabilistic models that act in a framework to reduce appliance usage data into contextual knowledge that accounts for variability in patterns in usage. Using sub-metered load data from various domestic wet appliances, the proposed technique is demonstrated learning the appliance operating likelihood surfaces from no prior knowledge.

ORCID iDs

Stephen, Bruce ORCID logoORCID: https://orcid.org/0000-0001-7502-8129, Galloway, Stuart ORCID logoORCID: https://orcid.org/0000-0003-1978-993X and Burt, Graeme ORCID logoORCID: https://orcid.org/0000-0002-0315-5919;
  • Item type:Article
    ID code:47542
    Dates:
    Date
    Event
    July 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:17 Apr 2014 08:20
    Last modified:11 Nov 2024 10:39
    Related URLs:
    URI:https://strathprints.strath.ac.uk/id/eprint/47542
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