Buildings represent 40% of the total energy consumption in the UK. For building owners, the day to day operation of a building amounts to about 70% of its total cost over its lifespan. And yet, approximately a third of commercial building space goes relatively unused. This presents a great opportunity for cost savings through space optimisation and energy consumption modelling in buildings. Mapping out and aggregating building data will enable operations staff to not only optimise energy use, but also to develop an effective asset management strategy.
This PhD project, run in collaboration with EDF Energy, will develop an innovative way to combine data from building management systems, specific building sensors (that record temperature, noise, light, and CO2 levels) and data from phones, in order to create an intelligent system that optimises building management. The project will improve energy optimisation by going beyond the simple linear methods (e.g. regression models) generally used in this context, to machine learning techniques such as neural networks and support vector machines, that can capture the complex nonlinear relationships that govern building energy consumption and occupancy processes.
The project will use the fine-grained data gathered by the sensor network — along with building information modelling (BIM) data — to visualise and develop insight into the building’s salient and subtle operating features. This insight will also be used to explore other applications of BIM in the operation of a building such as indoor mapping and emergency response, and how these might contribute to a larger asset management strategy. The connected visualisation platform created with the BIM model will be developed as a tool to showcase trends in the building’s occupancy and energy use to students and the university body. The initial focus for this work will be the University Library, but later on the student will apply their knowledge to the new buildings being constructed as part of the university’s £1B Smart Campus development project.
The successful candidate will hold a first or second class UK honours degree or equivalent in computer science, engineering, mathematics or statistics. The ideal candidate should have a good understanding of statistics and some knowledge of sensing technologies. An interest in machine learning, IoT and the built environment will be beneficial. The student will be jointly supervised by Prof. Matthew Chalmers (Computing Science) and Fiona Bradley (Civil Engineering), along with industrial advisors from EDF Energy. The student would visit EDF Energy’s R&D facility in London at least once every other month to deepen and broaden their knowledge, and to take advantage of the industrial research happening there.
Start Date: April 2018
Funding is available to cover tuition fees for UK/EU applicants, as well as paying a stipend at the Research Council rate (estimated £14,553 for Session 2017-18).