Skip to main content

A user behaviour-driven smart-home gateway for energy management

Vastardis, N., Kampouridis, Michael, Yang, K. (2016) A user behaviour-driven smart-home gateway for energy management. Journal of Ambient Intelligence and Smart Environments, 8 (6). pp. 583-602. ISSN 1876-1364. E-ISSN 1876-1372. (doi:10.3233/AIS-160403)

PDF - Author's Accepted Manuscript
Download (854kB) Preview
[img]
Preview
Official URL
http://dx.doi.org/10.3233/AIS-160403

Abstract

Current smart-home and automation systems have reduced generality and modularity, thus confining users in terms of functionality. This paper proposes a novel system architecture and describes the implementation of a user-centric smart-home gateway that is able to support home-automation, energy usage management and reduction, as well as smart-grid operations. This is enabled through a middleware service that exposes a control API, allowing the manipulation of the home network devices and information, irrespectively of the involved technologies. Additionally, the system places the users as the prime owners of their data, which in turn is expected to make them much more willing to install and cooperate with the system. The gateway is supported by a centralised user-centric machine-learning component that is able to extract behavioural patterns of the users and feed them back to the gateway. The results presented in this paper demonstrate the efficient operation of the gateway and examine two well-know machine learning algorithms for identifying patterns in the user’s energy consumption behaviour. This feature could be utilised to improve its performance and even identify energy saving opportunities.

Item Type: Article
DOI/Identification number: 10.3233/AIS-160403
Uncontrolled keywords: Smart gateway, middleware, system architecture, machine-learning, energy management
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Faculties > Sciences > School of Computing
Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Michael Kampouridis
Date Deposited: 19 Nov 2015 19:40 UTC
Last Modified: 29 May 2019 16:26 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/52101 (The current URI for this page, for reference purposes)
  • Depositors only (login required):

Downloads

Downloads per month over past year