Ant colony algorithm for building energy optimisation problems and comparison with benchmark algorithms

Bamdad, K; Cholette, ME; Guan, L; Bell, J

Ant colony algorithm for building energy optimisation problems and comparison with benchmark algorithms

Bamdad, K Cholette, ME Guan, L

Bell, J

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Publication Type:: Journal Article
Citation:: Energy and Buildings, 2017, 154 pp. 404 - 414
Issue Date:: 2017-11-01

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Field	Value	Language
dc.contributor.author	Bamdad, K	en_US
dc.contributor.author	Cholette, ME	en_US
dc.contributor.author	Guan, L https://orcid.org/0000-0003-2085-7928	en_US
dc.contributor.author	Bell, J	en_US
dc.date.issued	2017-11-01	en_US
dc.identifier.citation	Energy and Buildings, 2017, 154 pp. 404 - 414	en_US
dc.identifier.issn	0378-7788	en_US
dc.identifier.uri	http://hdl.handle.net/10453/123388
dc.description.abstract	© 2017 Elsevier B.V. In the design of low-energy buildings, mathematical optimisation has proven to be a powerful tool for minimising energy consumption. Simulation-based optimisation methods are widely employed due to the nonlinear thermal behaviour of buildings. However, finding high-quality solutions with reasonable computational cost remains a significant challenge in the building industry. In this paper, Ant Colony Optimisation for continuous domain (ACOR) is developed and applied to optimise a commercial building in Australia. The results for a typical commercial building showed that optimisation can achieve an additional energy savings of more than 11.4%, even after some common energy saving measures were implemented (e.g. double pane windows). The performance of ACOR was compared to three benchmark optimisation algorithms: Nelder-Mead (NM) algorithm, Particle Swarm Optimisation with Inertia Weight (PSOIW) and the hybrid Particle Swarm Optimisation and Hooke-Jeeves (PSO-HJ). This comparison showed that ACOR was able to consistently find better solutions in less time than the benchmark algorithms. The findings demonstrate that ACOR can further facilitate the design of low-energy buildings.	en_US
dc.relation.ispartof	Energy and Buildings	en_US
dc.relation.isbasedon	10.1016/j.enbuild.2017.08.071	en_US
dc.subject.classification	Building & Construction	en_US
dc.title	Ant colony algorithm for building energy optimisation problems and comparison with benchmark algorithms	en_US
dc.type	Journal Article
utslib.citation.volume	154	en_US
utslib.for	1202 Building	en_US
utslib.for	09 Engineering	en_US
utslib.for	12 Built Environment and Design	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building/School of Built Environment
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	154	en_US

Abstract:

© 2017 Elsevier B.V. In the design of low-energy buildings, mathematical optimisation has proven to be a powerful tool for minimising energy consumption. Simulation-based optimisation methods are widely employed due to the nonlinear thermal behaviour of buildings. However, finding high-quality solutions with reasonable computational cost remains a significant challenge in the building industry. In this paper, Ant Colony Optimisation for continuous domain (ACOR) is developed and applied to optimise a commercial building in Australia. The results for a typical commercial building showed that optimisation can achieve an additional energy savings of more than 11.4%, even after some common energy saving measures were implemented (e.g. double pane windows). The performance of ACOR was compared to three benchmark optimisation algorithms: Nelder-Mead (NM) algorithm, Particle Swarm Optimisation with Inertia Weight (PSOIW) and the hybrid Particle Swarm Optimisation and Hooke-Jeeves (PSO-HJ). This comparison showed that ACOR was able to consistently find better solutions in less time than the benchmark algorithms. The findings demonstrate that ACOR can further facilitate the design of low-energy buildings.

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http://hdl.handle.net/10453/123388