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Energy use predictions with machine learning during architectural concept design

Paterson, G; Mumovic, D; Das, P; Kimpian, J; (2017) Energy use predictions with machine learning during architectural concept design. Science and Technology for the Built Environment , 23 (6) pp. 1036-1048. 10.1080/23744731.2017.1319176. Green open access

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Abstract

Studies have shown that the actual energy consumption of buildings once built and in operation is often far greater than the energy consumption predictions made during design—leading to the term “performance gap.” An alternative to traditional, building physics based, prediction methods is an approach based on real-world data, where behavior is learned through observations. Display energy certificates are a source of observed building “behavior” in the United Kingdom, and machine learning, a subset of artificial intelligence, can predict global behavior in complex systems, such as buildings. In view of this, artificial neural networks, a machine learning technique, were trained to predict annual thermal (gas) and electrical energy use of building designs, based on a range of collected design and briefing parameters. As a demonstrative case, the research focused on school design in England. Mean absolute percentage errors of 22.9% and 22.5% for annual thermal and electrical energy use predictions, respectively, were achieved. This is an improvement of 9.1% for the prediction of annual thermal energy use and 24.5% for the prediction of annual electrical energy use when compared to sources evidencing the current performance gap.

Type: Article
Title: Energy use predictions with machine learning during architectural concept design
Open access status: An open access version is available from UCL Discovery
DOI: 10.1080/23744731.2017.1319176
Publisher version: http://doi.org/10.1080/23744731.2017.1319176
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Bartlett School Env, Energy and Resources
URI: https://discovery.ucl.ac.uk/id/eprint/10043119
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