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AI for optimisation and demand side response of built environment management

The project aims to improve aspects of Mitie Energy's existing Building Analytics and enhance its Demand Side Response capability. Both areas are seen as the key part of Mitie's energy strategy to deliver not only energy-saving solutions but also a fully-connected workplace in Building Management Systems (BMS).

The project will carry out R&D in two aspects:

  1. Building analytics (BA) - A big problem with BA is the initial data acquisition and translation of the BMS data flow into the rules of the BA engine. This work today is undertaken manually by skilled operators. This project will develop automated solutions to replace this manual element with mathematical techniques and self-learning algorithms. The project will also investigate the enhancement of the existing database rules with new energy-saving rules that extract the information/knowledge from the noisy data and optimise BMS energy use. This aspect constitutes the majority of the workload, and it comprises 3 main challenges:
    • Automatic BMS equipment group clustering and individual equipment consumption profiles.
    • Automatic BMS point tagging system using Natural Language Processing approach.
    • Advanced BMS rules for reducing energy consumption.
  2. Demand Side Response (DSR) - this is seen as a high revenue growth area for Mitie Energy, as addressing energy demand under the National Grid DSR programmes is well incentivised. This commercial challenge will be investigated together with the BA challenges. The initial stage of the DSR challenge has been completed and implemented in Mitie’s infrastructure, based on the paper entitled “Modelling Energy Demand Response Using Long-Short Term Memory Neural Networks” recently submitted for publication.

The project will continue to study both challenges and develop a series of advanced analytic techniques to deliver satisfactory solutions and system optimisation.


Meet the Principal Investigator(s) for the project

Dr Qingping Yang - Dr QingPing Yang is currently the Group Leader for Brunel Quality Engineering and Smart Technology (QUEST) Research Group and Robotics and Automation Research Group.  Dr Yang joined the Brunel Centre for Manufacturing Metrology (BCMM) in 1988 with a visiting scholarship awarded by the AVIC, after his graduation in Instrumentation and Measurement Technology from Chengdu Aeronautical Polytechnic in 1983 and subsequent 4 years’ research experiences at an Aircraft Structure Research Institute (AVIC, Xi’an) and admission to an MSc Programme in Robot Control and Intelligent Control at Northwestern Polytechnical University.  In 1989, he was awarded an ORS Award and a PhD Studentship from British Technology Group to develop a patented smart 3D high precision probe system for CMMs, and he received his PhD degree in October 1992.  Since then he has been working as a Research Fellow, Lecturer/Senior Lecturer/Reader (Associate Professor) at Brunel University London.  He has actively participated in 15 (11 as Principal Investigator) research projects funded by the UK government, EU and industrial companies, with a total funding of about £2.5 million as Principal Investigator and £888K as Co-Investigator.  Through more than 30 years dedicated research, he has developed a unique and coherent research field broadly integrating three research areas of sensor/measurement systems, quality engineering and smart technologies (including AI and robotics) with rigorous theoretical foundation, addressing the core science and technology underpinning these areas.  He has published more than 110 journal/conference papers, 5 book chapters and 3 patents (one patent successfully assigned for commercial exploitation in 2004) in these areas. He has supervised (as the 1st supervisor) 20 PhD and 3 MPhil students with successful completion as well as 9 visiting academic staff / PhD students, and he is currently supervising one postdoctoral Research Fellow and 8 PhD students. Dr Yang has received numerous prizes and awards for outstanding academic and work performance in the past (including three performance bonuses in Brunel University). He has been a member of IEEE and IET. He was profiled in the 15th edition of Marquis Who’s Who in the World (1998) and the 5th edition of Marquis Who’s Who in Science and Engineering (2000).

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Project last modified 28/06/2021