Title: Intelligent Agents for Home Energy Management
PI: Dr Rogers, University of Southampton
Fund: £813k TEDDI longer
Project lifespan: Nov 2010 to March 2014
Aims: This project seeks to apply novel artificial intelligence approaches to develop intelligent agents that will enable domestic consumers to visualise, understand and manage their energy use. The project brings together expertise in artificial intelligence and software agents, renewable energy and energy efficiency in the built environment, and human factors in the design of automated control and feedback systems at the University of Southampton to address the challenge described above. The initial work in the project has three foci, centering on these three core competencies:
1. Intelligent Agent Development This work package will focus on the development of intelligent software agents that are able (i) to learn both the thermal characteristics of the building in which they are deployed and the day to- day behaviour and energy demands of the householders, (ii) to optimise the use of energy given householders’ individual preferences regarding cost, comfort and carbon, and (iii) to model the impact of behaviour and building infrastructure changes.
2. Energy Use Characterisation This work package will develop the thermal models that the agents will build, providing initial parameters for different classes of buildings to bootstrap the agent’s online learning process, in order that the home energy management agent can autonomously model the effects of various interventions (such as behaviour changes or infrastructure changes to the home itself).
It will also characterise the energy consumption and generation of homes, depending on the various forms of heating, renewable micro-generation and storage devices that may be available to the agents within the scenarios considered.
3. Human Factors in Behaviour Change This work package will address the fact that it is humans, and not buildings, that consume energy. As such, it will explore how the agents should interact with the householders in order to encourage behaviour change, how the householders’ preferences and future demand requirements should be elicited in efficient and nonintrusive ways, and how feedback should be provided when autonomous decisions are made by the agent.
Ms Kirsten Revell
Drew Smith Ltd
Energy Savings Trust
Rogers, A., Ghosh, S., Wilcock, R. and Jennings, N. R. (2013) A Scalable Low-Cost Solution to Provide Personalised Home Heating Advice to Households. In: The Fifth ACM Workshop On Embedded Systems For Energy-Efficient Buildings (BuildSys).
Revell, K. M. A. and Stanton, N. A. (2013) Case studies of mental models in home heat control: Searching for feedback, valve, timer and switch theories. In: Applied Ergonomics, 2013,
Revell, K. M. A. and Stanton, N. A. (2013) Using the notion of mental models in design to encourage optimal behaviour in home heating use. In: 11th International Conference on Naturalistic Decision Making 2013, Marseille, France, NDM2013, France.