Ant colony algorithm for building energy optimisation problems and comparison with benchmark algorithms
Article
Article Title | Ant colony algorithm for building energy optimisation problems and comparison with benchmark algorithms |
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ERA Journal ID | 4185 |
Article Category | Article |
Authors | Bamdad, Keivan (Author), Cholette, Michael E (Author), Guan, Lisa (Author) and Bell, John (Author) |
Journal Title | Energy and Buildings |
Journal Citation | 154, pp. 404-414 |
Number of Pages | 11 |
Year | 2017 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 0378-7788 |
1872-6178 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.enbuild.2017.08.071 |
Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0378778817313324 |
Abstract | 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. |
Keywords | Optimisation algorithm benchmarking, Building optimisation, Ant colony optimisation, Particle swarm optimisation, Australian commercial building |
ANZSRC Field of Research 2020 | 409999. Other engineering not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Queensland University of Technology |
University of Technology Sydney | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q5x1x/ant-colony-algorithm-for-building-energy-optimisation-problems-and-comparison-with-benchmark-algorithms
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