Dr Bruce Stephen

Strathclyde Chancellor's Fellow

Electronic and Electrical Engineering

Personal statement

I joined the University in 1998 and came to work in the Department of Electronic and Electrical Engineering in April 1999. Previously, I had studied Aeronautical Engineering at Glasgow University at undergraduate level and then undertook postgraduate study in the department Computer Science at Strathclyde, gaining an MSc here in 1998. My current research focuses on developing analytical tools to support advances in power systems operation and management such as increasing plant reliability, integrating renewable generation and understanding energy end use. I am currently an investigator on the EU FP7 Orchestrating Renewable Integrated Generation in Neighbourhoods (ORIGIN) project and co-investigator on the EPSRC BuildTEDDI Aging Population Attitudes to Sensor Controlled Home Energy (APAtSCHE) project. I teach two courses on the Electrical Mechanical Engineering degree stream: Engineering Computing and Integrated Design as well as supervising project work undertaken by 4th year, 5th year, MSc and Centre for Doctoral Training PhD students.


Classification and characterization of intra-day load curves of PV and non-PV households using interpretable feature extraction and feature-based clustering
Hu Maomao, Telford Rory, Ge Dongjiao, Stephen Bruce, Wallom David CH
Sustainable Cities and Society Vol 75 (2021)
Semi-automated knowledge capture and representation for the development of knowledge based systems
Young A, West G, Brown B, Stephen B, Michie C, McArthur S, Duncan A
12th Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies (NPIC&HMIT 2021) (2021)
Symbolic representation of knowledge for the development of industrial fault detection systems
Young Andrew, West Graeme, Brown Blair, Stephen Bruce, Michie Craig, McArthur Stephen
International Congress and Workshop on Industrial AI 2021 (2021)
Weather related fault prediction in minimally monitored distribution networks
Tsioumpri Eleni, Stephen Bruce, McArthur Stephen D J
Energies Vol 14 (2021)
Model-free non-invasive health assessment for battery energy storage assets
Sobon Joanna, Stephen Bruce
IEEE Access Vol 9, pp. 54579-54590 (2021)
A probabilistic model for characterising heat pump electrical demand versus temperature
Anderson Amy, Stephen Bruce, Telford Rory, McArthur Stephen
2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe) 2020 IEEE PES Innovative Smart Grid Technologies Europe, pp. 1030-1034 (2020)

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Research interests

My main research focus is on the application of Machine Learning to intensive condition monitoring applications in the power and agriculture sectors to automate condition assessment, anomalous condition detection and simulation of future behaviours.

Current research activities can be broadly divided into the following areas:

  • Machine Learning applications in power system condition monitoring
  • Distributed information systems for power system condition monitoring and asset management
  • Modelling and analysis of wind energy systems
  • Analysis and automation methods in demand side energy management
  • Machine Learning applications in animal welfare
  • Wireless ad-hoc networking for livestock monitoring

These include collaborations outside of InstEE with CIDCOM, SAC Easter Bush and Embedded Technology Solutions Ltd.

Professional activities

QFF Quarterly Forecasting Forum, June 2018
Invited Talk - New Thames Valley Vision Workshop
Invited speaker
Invited Talk - SGEM Load Modelling Workshop
External Referee - Research Foundation Flanders (FWO)
External Examiner
Hisatoshi Ikeda
IEEE Transactions on Power Systems (Journal)
Peer reviewer

More professional activities


Supporting Analytics for Nuclear Asset Data Lifecycle
Stephen, Bruce (Principal Investigator) Brown, Blair David (Co-investigator)
01-Jan-2020 - 30-Jan-2024
Industrial Case Account - University of Strathclyde 2020 | Blair, Jennifer
Stephen, Bruce (Principal Investigator) Brown, Blair David (Co-investigator) Blair, Jennifer (Research Co-investigator)
01-Jan-2020 - 01-Jan-2023
Analytical Middleware for Informed Distribution Networks (AMIDiNe)
Stephen, Bruce (Principal Investigator) Browell, Jethro (Co-investigator) Galloway, Stuart (Co-investigator) Wallom, David (Co-investigator)
The programme of research that constitutes AMIDiNe will devise analytics that link point measurement to whole system to address the increasingly problematic management of electrical load on distribution networks as the UK transitions to a low carbon energy system. Traditionally, distribution networks had no observability and power flowed from large generation plant to be consumed by customers in this 'last mile'. Now, and even more so in future, those customers are generators themselves and the large generators that once supplied them have been supplanted by intermittent renewables. This scenario has left the GB energy system in position where it is servicing smaller demands at a regional or national level but faces abrupt changes in the face of weather and group changes in load behaviour, therefore it needs to be more informed on the behaviour of distribution networks. The UK government's initiative to roll out Smart Meters across the UK by 2020 has the potential to illuminate the true nature of electricity demand at the distribution and below levels which could be used to inform network operation and planning. Increasing availability of Smart Meter data through the Data Communications Company has the potential to address this but only when placed within the context of analytical and physical models of the wider power system - unlike many recent 'Big Data' applications of machine learning, power systems applications encounter lower coverage of exemplars, feature well understood system relations but poorly understood behaviour in the face of uncertainty in established power system models.

AMIDiNe sets out its analytics objectives in 3 interrelated areas, those of understanding how to incorporate analytics into existing network modelling strategies, how go from individual to group demand behavioural anticipation and the inverse problem: how to understand the constituent elements of demand aggregated to a common measurement point.

Current research broadly involving Smart Metering focuses on speculative developments of future energy delivery networks and energy management strategies. Whether the objective is to provide customer analytics or automate domestic load control, the primary issue lies with understanding then acting on these data streams. Challenges that are presented by customer meter advance data include forecasting and prediction of consumption, classification or segmentation by customer behaviour group, disambiguating deferrable from non-deferrable loads and identifying changes in end use behaviour.

Moving from a distribution network with enhanced visibility to augmenting an already 'smart' transmission system will need understanding of how lower resolution and possibly incomplete representations of the distribution network(s) can inform more efficient operation and planning for the transmission network in terms of control and generation capacity within the context of their existing models. Improving various distribution network functions such as distribution system state estimation, condition monitoring and service restoration is envisaged to utilise analytics to extrapolate from the current frequency of data, building on successful machine learning techniques already used in other domains. Strategic investment decisions for network infrastructure components can be made on the back of this improved information availability. These decisions could be deferred or brought forward in accordance with perceived threats to resilience posed by overloaded legacy plant in rural communities or in highly urbanised environments; similarly, operational challenges presented by renewable penetrations could be re-assessed according to their actual behaviour and its relation to network voltage and emergent protection configuration constraints.
01-Jan-2019 - 31-Jan-2022
Onyx Work Order System
McMillan, David (Principal Investigator) Salo, Erik (Co-investigator) Stephen, Bruce (Co-investigator)
08-Jan-2019 - 08-Jan-2019
Feasibility of feeder dynamic plase balancing using flexible loads
Stephen, Bruce (Principal Investigator) Jennett, Kyle (Co-investigator)
21-Jan-2019 - 20-Jan-2019
AGILE - Aggregators as diGital Intermediaries in Local Electricity markets: EPSRC/ESC Follow on Funding
Galloway, Stuart (Principal Investigator) Irvine, James (Co-investigator) Stephen, Bruce (Research Co-investigator)
01-Jan-2018 - 30-Jan-2020

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