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Nigel Goddard and Charles Sutton awarded £2M grant; RAs needed

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Grant to apply Machine Learning methods to reducing energy demands

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. Nigel Goddard and Charles Sutton will work with other Informatics academics, building engineers and sociologists 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. 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. There are considerable machine learning challenges in inferring behaviours from the stream of noisy, indirect sensor data.

For more details, including jobs available, see the project website: