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A close-up of Broadcasting Place, City Campus


To meet the Government’s target of reducing CO2 emissions by 80 per cent by 2050, existing UK housing stock will need to undergo deep, rapid and large-scale retrofit in order to drive down energy consumption while safeguarding the occupants’ quality of life.

This research theme is focused on:

  • Understanding power supply, energy recovery, renewable energy, microgeneration, networks and building management systems.
  • Developing an understanding of the relationships between buildings and occupant behaviour.
Research Expertise
  • Melanie Smith FRICS
    Melanie’s background as a Chartered Building Surveyor has taken her through building control and regulation, surveying consultancy professional practice, academic course leader and lecturer, and research.
  • Dr Anne Stafford
    Dr. Anne Stafford is a Senior Research Fellow at the Centre for the Built Environment (CeBE), with particular interests in Renewable Technologies, Building Energy Use, and System Performance Monitoring.
  • Professor Jiamei Deng
    Professor Deng joins Leeds Beckett as Professor of Artificial Intelligence, Control and Energy as part of the University’s Leeds Sustainability Institute. Her research interests lie in improving efficiency for buildings; renewable energy technologies; safety monitoring for nuclear power plants, the manufacturing industry and transport systems; virtual sensor design; and data analysis.

Case Studies

Smart Online Monitoring of Nuclear Power Plant

Both existing and new design Nuclear Power Plants (NPP) strive to improve safety, maintain availability and reduce the cost of operation and maintenance, however plant life extensions and power updates push the demand for the new tools of diagnosing the health of NPP.

Monitoring the status of plants by diverse means has become the norm, however there are very few reliable monitoring tools used in control room operator desks in nuclear plants. This is due to the research challenge in the modelling area and the task of verification and validation of such tools for accident progression.

Smart Online Monitoring, a two-year project of four project partners with the Engineering and Physical Sciences Research Council led by Professor Deng, aims to employ computational intelligence methods to monitor accident progression, predict the onset and evolution of an accident, and support operators in their decision-making process using available information from the plant’s online monitoring database. The tool will improve safety, maintain plant availability and reduce accident handling costs in nuclear power plants.

IEA EBC Annex 67: Energy Flexible Buildings

Leeds Beckett University is currently leading Sub-Task C (Demonstration and User Perspectives) of the IEA EBC Annex 67 on Energy Flexible Buildings (overall lead: Danish Technical Institute). This is a consortium consisting of universities and commercial organisations from over 15 countries across Europe and further afield. The project covers all aspects of the ability of buildings to modify demand (in terms of both quantity and timing) in response to dynamic grid requirements, also taking into account changing needs associated with increased penetration of intermittent renewable generators and increased electrical consumption due to technologies such as heat pumps and electric vehicles. The Annex is expected to continue until September 2019.

CCC (Carbon, Control and Comfort)

The Carbon Control and Comfort (CCC) Project is a collaborative 3-year research project funded by EPSRC in partnership with E.ON. The academic partners involved are: UCL (lead partners), Leeds Beckett University, Loughborough University, De Montfort University, Kings College London, Cardiff University and the University of Greenwich.

For more information please visit our research case studies webpages.

Robustness of Transient Emissions Model

Significant quantities of emissions pose a threat to our health and increasingly stringent regulations require organisations to reduce these hazardous levels. Emissions prediction has always been a major challenge to the industry because available technologies for reduction are heavily dependent on measurement technology and emissions can only be reduced if the emission level is known. However a simple way to handle estimations is to use computational intelligence methods.

The Robustness of Transient Emissions Model, a one-year project funded by Royal Academy of Engineering, aimed to employ neural networks to predict emission levels accurately, even when the composition of contributing sources changes.

The major outcome of the project was that a new emission prediction method has been proposed. The solution recommends the use of a virtual sensor to estimate the emissions and to trigger a cycle reducing the level when the emission threshold has been reached.