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Life-cycle data analysis in buildings

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  • Full or part time
    Dr James O’Donnell
  • Application Deadline
    No more applications being accepted
  • Funded PhD Project (European/UK Students Only)
    Funded PhD Project (European/UK Students Only)

About This PhD Project

Project Description

Research Field: Holistic Environmental and Energy Management in Buildings.

Objective: To develop new life-cycle analytical techniques to process and mine “Big Data” in buildings, e.g. large volumes of time-series data in the form of benchmarks generated by predictive BPS models and actual performance measured by building instrumentation.

Context: Optimum building operation requires analysis of a relatively large number of data sets when compared with traditional analysis in the buildings industry. Each analysis action should focus on one or more contexts that are important to the stakeholder in question [29]. As an example, an energy modeller may have an interest in the performance of a system, or a building manager may only require high-level performance data for a building. Another advantage is that these contexts are identical to the contexts defined in previously defined performance specification ontology which in turn enables automated data processing.

Methodology: The precise research methodology is not necessarily prescribed at this point, but is likely to consider some or all of the following research issues/stages:
• Through an extensive literature review this work will determine the most appropriate technique for handling large volumes of predicted and measured building performance data over the entire life-cycle of a building.
• The identified technique will extend complex event processing as used by the researcher in previous publication in accordance with established industry algorithms.
• Methodology Development: Develop an appropriate methodology for assessing the holistic environmental and energy performance over the life-cycle of a building. The methodology is likely to be a combination of data mining, statistical and physics based techniques.
• Methodology Assessment: Using the developed methodology, examine the effectiveness of the proposed analytical solutions from a technical and economic perspective.

Applicants should have an upper level Bachelors or Masters degree in Engineering, Computer Science or a similar cognate discipline to be eligible to enrol in an Engineering PhD programme at University College Dublin. Experience in building performance simulation modelling, data mining techniques, statistics would be advantageous. This position can commence any time from May 2014 and is open to individuals from the EU. The position is for 4 years and is re-numerated in accordance with standard UCD norms. Written applications, in English, should include a concise C.V., a one page letter of motivation describing why you are interested in this position, an English language test certificate, if applicable and contact details for three references. Along with the application, please include a copy of relevant qualifications such as official university transcripts. Please email applications to Dr James O’Donnell ([Email Address Removed]) by the deadline 28 February, 2014.

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