Researcher uses AI to predict building energy usage


A PhD researcher at the University of Huddersfield is using artificial intelligence to better predict building energy usage, in the hopes of minimising the energy performance gap

Rima Alaaeddine, a researcher within the University’s School of Art Design and Architecture, aims to tackle the energy performance gap and could benefit the building sector at a time when there is increasing pressure on industries around the world to conserve their energy consumption.

This week the government announced it wants to halve the energy consumption of new buildings by 2030 as part of its clean growth grand challenge mission.

Alaaeddine’s research could play an important part in helping the construction industry meet these strict energy efficiency targets. With the energy consumption of buildings accounting for 30% of global energy use, improving the energy efficiency of buildings is a key strategic objective.

More accurate energy predictions can facilitate building energy optimisation and guide decisions regarding the building energy performance.

“My research will employ a branch of artificial intelligence (AI) entitled machine learning,” said Alaaeddine.

She explains that machine learning techniques are capable of handling complex and non-linear problems and can offer more accurate predictions on occupants’ behaviour

Her project is already receiving national recognition. The 27-year-old researcher was shortlisted from hundreds of applicants from across the UK to present her research in Parliament, as part of the annual STEM for Britain competition, to a range of politicians and a panel of expert judges.

The prestigious poster competition, headed by the Parliamentary and Scientific Committee, was organised in collaboration with the Royal Academy of Engineering, the Royal Society of Chemistry, the Institute of Physics, the Royal Society of Biology, The Physiological Society and the Council for the Mathematical Sciences.

Alaaeddine’s entry was entitled ‘Minimizing the energy performance gap by application of an integrative machine learning methodology for occupants’ behaviour prediction’ and she says it was an honour taking part in STEM for Britain and to be given the opportunity to present her work in Parliament.

“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,” she said.


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