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

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.