Reducing energy demand from existing dwellings through occupant behaviour change is crucial for meeting UK carbon emission reduction targets. Dwellings account directly for 32% of UK energy consumption, and corresponding carbon emissions. While there are many reduction efforts aimed at new-build, a focus on existing dwellings is essential: 80% of the dwellings that will be in place in the UK in 2050 are already built. Attention to behaviour change is important - behavioural differences are estimated by DECC to account for 60% of the variance in demand. Demand related to heat is key - 80% of domestic energy demand is for heating. Using an interdisciplinary conceptual framework, our team of computer scientists, building engineers and sociologists will work together to explore the interaction of energy technologies and householder energy behaviours. For the first time household energy demand will be able to be analysed in great detail across a large number of homes and the effect of behavioural feedback evaluated over a multi-year period. The Smart Meter rollout planned to be complete by 2020 is intended to encourage householders to reduce their energy demand. These meters and the associated monitors create a feedback loop to householders in which energy-consumption information from the meters is provided to the householder on the monitor in the hope that this will cause him or her to change behaviours to reduce the amount of energy used, or the amount of money spent on energy, or the associated carbon emissions. This project's main goal is to construct an enhanced feedback loop which provides information to householders not just on their energy consumption, but also on what activities they are using energy, how much for each one, together with suggestions for what they might do to reduce their energy expenditure and use. We would hope to be able to tell the householder things like: "Last week you spent £10 on hot water for showers", or "Yesterday you spent £4 on heating your flat, if you turned off the heating at night you would probably have only spent £3 - you could save around £250 a year by doing this". We will construct this feedback loop and evaluate its effectiveness compared to standard Smart Meter type feedback by involving hundreds of households in a study over a three year period. We will involve a variety of types of households including single people, multi-adult dwellings, and families, and expect to have participants across income brackets. The feedback loop will use small unobtrusive wireless sensors in the dwellings to record data and transmit it over the internet to a large secure database; and a tablet PC to provide information back to householders. The data will be processed by software to tell the occupants how much energy, carbon and money they are spending on which energy-related activities - for example over the last day, week, month, and year. This feedback loop will run for several years (up to 3) and will provide the participants with a wealth of information that they can use to reduce their energy expenditure. We will compare how effective this feedback is with that provided by Smart Meters, that does not break down energy use into the important energy-using behaviours (particularly for gas use). At the end of the study we will ask participants if we can use the data we have gathered, with all personal information removed, in future studies. Those that agree will be contributing to a database that will be invaluable for future research efforts by us and others. 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. This will contribute to reducing energy poverty as well as the challenge of meeting UK 2050 carbon emission targets.

Overview: We propose a Centre for Doctoral Training in Data Science. Data science is an emerging discipline that combines machine learning, databases, and other research areas in order to generate new knowledge from complex data. Interest in data science is exploding in industry and the public sector, both in the UK and internationally. Students from the Centre will be well prepared to work on tough problems involving large-scale unstructured and semistructured data, which are increasingly arising across a wide variety of application areas. Skills need: There is a significant industrial need for students who are well trained in data science. Skilled data scientists are in high demand. A report by McKinsey Global Institute cites a shortage of up to 190,000 qualified data scientists in the US; the situation in the UK is likely to be similar. A 2012 report in the Harvard Business Review concludes: "Indeed the shortage of data scientists is becoming a serious constraint in some sectors." A report on the Nature web site cited an astonishing 15,000% increase in job postings for data scientists in a single year, from 2011 to 2012. Many of our industrial partners (see letters of support) have expressed a pressing need to hire in data science. Training approach: We will train students using a rigorous and innovative four-year programme that is designed not only to train students in performing cutting-edge research but also to foster interdisciplinary interactions between students and to build students' practical expertise by interacting with a wide consortium of partners. The first year of the programme combines taught coursework and a sequence of small research projects. Taught coursework will include courses in machine learning, databases, and other research areas. Years 2-4 of the programme will consist primarily of an intensive PhD-level research project. The programme will provide students with breadth throughout the interdisciplinary scope of data science, depth in a specialist area, training in leadership and communication skills, and appreciation for practical issues in applied data science. All students will receive individual supervision from at least two members of Centre staff. The training programme will be especially characterized by opportunities for combining theory and practice, and for student-led and peer-to-peer learning.