EDF Energy has an R&D centre in the UK with around 50 researchers in situ. The centre is an integral part of the EDF Group R&D network and works across the whole value chain of electricity; successfully leading on millions of pounds worth of international projects. It is a centre of expertise for the group benefiting from the UK's strong experience in a number of topics including Offshore Wind, Intermittency or Smart Grids and works across all EDF Energy specialisms including Nuclear, CCS, Energy Efficiency, Smart Meters, Electric Vehicles, and Digital Futures.
The R&D Centre works in partnership with a number of universities across the UK to sponsor post-graduate research projects. Information will be provided here on EDF Energy sponsored research opportunities when they arise.
- A first or second class UK honours degree or equivalent in computer science, engineering, mathematics or statistics.
- A good understanding of statistics and some knowledge of sensing technologies.
- An interest in machine learning, IoT and the built environment would be beneficial.
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.