About the Project
Buildings are huge energy consumers of our society when providing people with comfortable living and working environment. However, most of them are currently not working efficiently, with respect to both controlling the environment and consuming energy. Current building control systems are generally based on simple control algorithms such as turning on air-conditioning when the indoor temperature reaches a pre-defined threshold. This simplified method will not provide occupants with comfortable indoor environment, while consuming excessive amount of energy.
This PhD project will be built upon inter-disciplinary collaborations between building services engineers and computer scientists, aiming to develop a user-centred building control system that can well balance people’s comfort and building energy consumption. The candidates of this PhD position should have a relevant education background, such as building services engineering, computer science, mechatronic engineering etc. This study will concentrate on, 1) well understanding occupants’ comfort and behavioural preferences in an example building (preferably to be an office building); 2) developing control algorithms according to 1); 3) implementing the algorithms developed in 2) in a pilot room and test their performance on people’s comfort and building energy consumption.
Please note eligibility requirement:
* Academic excellence of the proposed student i.e. normally an Honours Degree: 1st or 2:1 (or equivalent) or possession of a Masters degree, with merit (or equivalent study at postgraduate level). Applicants may also be accepted on the basis of relevant and substantial practitioner/professional experience.
* Appropriate IELTS score, if required.
For further details of how to apply, entry requirements and the application form, see https://www.northumbria.ac.uk/research/postgraduate-research-degrees/how-to-apply/
Please ensure you quote the advert reference above on your application form.
Application deadline: 1 November 2016
Start Date: March 2017
References
[1] Pan, S., C. Xu, S. Wei*, T. M. Hassan, L. Xie, Y. Xiong, S. Firth, D. Greenwood and P. de Wilde (2016). “Improper Window Use in Office Buildings: Findings from a Longitudinal Study in Beijing, China”. Energy Procedia 88: 761-767.
[2] Wei, S., T. M. Hassan, S. K. Firth and F. Fouchal (2016). "Impact of occupant behaviour on the energy-saving potential of retrofit measures for a public building in the UK". Intelligent Buildings International: 1-11.
[3] Wei, S., Jones, R. & de Wilde, P. (2014) “Driving factors for occupant-controlled space heating in residential buildings”. Energy and Buildings, 70 (0). pp 36-44.
[4] Wei, S., Buswell, R. & Loveday, D. (2013) “Factors affecting ‘end-of-day’ window position in a non-air-conditioned office building”. Energy and Buildings, 62 (0). pp 87-96.
[5] Underwood, C. (2015) “Fuzzy multivariable control of domestic heat pumps”. Applied Thermal Engineering, 90. pp. 957-969. ISSN 1359 4311.
[6] Neoh, S.C., Zhang, L., Mistry, K., Hossain, M.A., Lim, C.P., Aslam, N and Kinghorn, P. (2015). Intelligent Facial Emotion Recognition Using a Layered Encoding Cascade Optimization Model. Applied Soft Computing (Elsevier). Volume 34, September 2015, 72–93.
[7] Zhang, Y., Zhang, L., Neoh, S.C., Mistry, K. and Hossain, A. (2015) Intelligent affect regression for bodily expressions using hybrid particle swarm optimization and adaptive ensembles. Expert Systems with Applications, 42 (22). pp. 8678-8697.
[8] Zhang, L., Mistry, K., Jiang, M., Neoh, S.C. and Hossain, A. (2015) Adaptive facial point detection and emotion recognition for a humanoid robot. Computer Vision and Image Understanding, 140. pp. 93-114.
[9] Farid, D., Zhang, L., Hossain, A.M., Rahman, C.M., Strachan, R., Sexton, G. and Dahal, K. (2013). An Adaptive Ensemble Classifier for Mining Concept-Drifting Data Streams. Expert Systems with Applications, Vol 40, Issue 15. 5895-5906.
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