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Rima Alaaeddine

AI Machine Learning Used in PhD Research

PhD researcher Rima Alaaeddine, within University of Huddersfield’s School of Art Design and Architecture, has focused her work on how to combat and minimise the ‘energy performance gap’. Using AI’s Machine Learning, her research could benefit the building sector at a time when there is increasing pressure on industries around

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BDC 319 : Aug 2024

Rima Alaaeddine

AI Machine Learning Used in PhD Research

PhD researcher Rima Alaaeddine, within University of Huddersfield’s School of Art Design and Architecture, has focused her work on how to combat and minimise the ‘energy performance gap’. Using AI’s Machine Learning, her research could benefit the building sector at a time when there is increasing pressure on industries around the world to conserve their energy consumption. ‘Energy performance gap’ is used when a building consumes more energy than it was initially predicted during design phase. The gap is attributed to a set of variables such as environmental conditions, building characteristics and occupancy. Predicting how much energy a building’s occupants will consume, including lighting, hot water, electricity, appliances and the way they interact with the building for example, opening windows and controlling their heating, ventilation and air conditioning systems, is a complex task. For this reason, Rima’s research could play an important part in helping the construction industry meet strict energy efficiency targets, recently set by the UK Government as part of a new energy strategy. With the energy consumption of buildings accounting for 30% of the entire global energy use, improving the energy efficiency of buildings is one of the key strategic objectives. More accurate energy predictions can facilitate building energy optimisation and guide decisions regarding the building energy performance. In her research, she uses AI entitled Machine Learning, which are capable of handling complex and non-linear problems and can offer more accurate predictions on occupants’ behaviour. Rima’s project is already receiving national recognition and she has been shortlisted to present her research in Parliament, as part of the annual STEM for BRITAIN competition. Her entry is called ‘Minimising the energy performance gap by application of an integrative machine learning methodology for occupants’ behaviour prediction’. “The event provided me with an opportunity to communicate my research as widely as possible, to inform and enthuse non-scientific audiences about my research in the building energy performance realm, aiming to unveil the benefits it brings,” Rima said.

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