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PhD project title |
The real-life performance of energy efficiency technologies unearthed through data science. |
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Outline description, including interdisciplinary, intersectoral and international dimensions |
The use of data science for understanding the performance of energy efficiency technologies. In an effort to reduce the expenditure related to space heating/hot water and to lower the environmental impact, technologies such as heat pumps and mechanical ventilation heat recovery systems have been installed at many recent homes and may be considered for retrofitted into existing buildings, with numerous challenges experienced. In this project we collaborate with the largest housing association in Northern Ireland (Choice) to gather critical data and apply data analytics to identify the variables and combinations of variables that influence the performance of such technologies. Social housing associations work with a limited budget and a large proportion of their tenants are low income families who are more susceptible to experiencing fuel poverty. It is therefore essential that their intervention brings a positive change to tenants’ budget and their life. This project can make a real and lasting impact on the social and private housing sector. The student will develop skills on sensor/imaging technology and instrumentation, energy performance modelling and data analytics. Student will work closely with Choice and their contractors to understand the scale of the problem, scope and implement data collection protocol, undertake tenant liaison and deliver project outcomes at various national and international arena. Four key aspects that will be considered in this project are the influence of building envelope, microclimate, occupant behaviour and training. The project will contribute to the energy efficiency strategy of the housing associations and help optimise/streamline their retrofit plans. Further this work is expected to inform the design of new buildings, and especially capitalise on the modular offsite construction boom expected in GB/RoI on the social housing sector. The student will present at International Conferences |
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Key words/descriptors
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Data analytics, energy performance, climate change, occupant behaviour, training, thermal performance of buildings, multivariate analysis. |
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Fit to CITI-GENS theme(s) |
Use of Big Data, data science, and data analytics to inform and focus retrofitting schemes rolled out by Government. Further the project will create an awareness and increased participation from the occupants in the energy performance measures. The proposed project does not address Ethical aspects directly. |
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Supervisor Information
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First Supervisor: Dr. Sree Nanukuttan School: Natural and Built Environment Second Supervisor: Dr. Danielle Soban School: Mechanical and Aerospace Engineering Third Supervisor: Brian Rankin Company: Choice Housing Ireland |
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Name of non-HEI partner(s) |
Non HEI partner for this project will be Choice Housing Ireland and the lead contact/supervisor will be Mr. Brian Rankin, Energy Manager Choice Housing Ireland Ltd, Belfast
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Contribution of non-HEI partner(s) to the project:
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Choice is the largest housing association in Northern Ireland with over 10,000 properties to their portfolio. They are responsible for the upgrading energy performance of buildings for their tenants and have previously worked with the QUB supervisory team on an EPSRC project. Choice will provide training and placement for the candidate to understand the market, their role and challenges. Student will be working with Choice to select suitable properties from their portfolio for the project, liaise with tenants, contractors for installing sensors and roll out data collection protocol. Student will report to relevant bodies in Housing Association, review current regulations and contribute to new regulations and best practice. |
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Subject area |
Engineering – Building Science |