Reducing energy demand from existing dwellings through occupant behaviour change is crucial for meeting UK carbon emission reduction targets and will help the increasing numbers of households struggling to pay their energy bills.  Our team of computer scientists, building engineers and sociologists will work together to explore the interaction of energy technologies and householder behaviours related to energy in a sample of 600 dwellings.  Using wireless sensing, machine learning, and natural language generation technologies, we will construct an intelligent advice loop that will provide information to householders on what activities they are engaging in which use energy and how much energy is used for each one, together with suggestions for what they might do to reduce their energy expenditure; for example: "Last week you spent £10 on hot water for showers, if you reduced your average shower time from 15 minutes to 12 minutes you could save £100 per year".  If we can show that this loop is effective in helping people to reduce their energy demand, then we expect that energy suppliers and other companies will start to offer it as a service to households to help them keep their energy costs down.

For more details on the project see the Project section of the website.

For more details on the researchers involved, see the Investigators and Staff section.