Smart Home Energy Management

Smart Home Energy Management

David Lillis (University College Dublin, Ireland), Tadhg O'Sullivan (University College Dublin, Ireland), Thomas Holz (University College Dublin, Ireland), Conor Muldoon (University College Dublin, Ireland), Michael J. O'Grady (University College Dublin, Ireland) and Gregory M. P. O'Hare (University College Dublin, Ireland)
DOI: 10.4018/978-1-4666-7284-0.ch010


Autonomically managing energy within the home is a formidable challenge, as any solution needs to interoperate with a decidedly heterogeneous network of sensors and appliances, not just in terms of technologies and protocols but also by managing smart as well as “dumb” appliances. Furthermore, as studies have shown that simply providing energy usage feedback to homeowners is inadequate in realising long-term behavioural change, autonomic energy management has the potential to deliver concrete and lasting energy savings without the need for user interventions. However, this necessitates that such interventions be performed in an intelligent and context-aware fashion, all the while taking into account system as well as user constraints and preferences. Thus, this chapter proposes the augmentation of home area networks with autonomic computing capabilities. Such networks seek to support opportunistic decision-making pertaining to the effective energy management within the home by seamlessly integrating a range of off-the-shelf sensor technologies with a software infrastructure for deliberation, activation, and visualisation.
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System Architecture

The AUTHENTIC system follows a component-based software engineering approach adhering to the OSGi framework and is made up of five different modules (see also Figure 1):

Figure 1.

AUTHENTIC system architecture

  • The communications module, which provides a unified interface to all sensors and actuators in the HAN;

  • The semantic module, which infers situational context from low-level sensor data;

  • The deductive module, which realises intelligent decision-making using the SIXTH middleware together with a multi-agent systems approach;

  • The appliance scheduling module, which employs a constraint-based reasoning engine to schedule appliances based on different user preferences;

  • The AUTHENTIC graphical user interface (GUI), which allows the occupant to interact with all facets of the system.

The following sections provide an overview over each of these modules, before a more in-depth look is taken at some of the enabling technologies of the deductive component, which is the focus of this paper.

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