Building energy optimisation under uncertainty using ACOMV algorithm
Article
Article Title | Building energy optimisation under uncertainty using ACOMV algorithm |
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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 | 167, pp. 322-333 |
Number of Pages | 12 |
Year | 2018 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 0378-7788 |
1872-6178 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.enbuild.2018.02.053 |
Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S0378778817315943?via%3Dihub |
Abstract | This study develops a new scenario-based optimisation methodology to address building parameter uncertainty. A multi-objective optimisation problem based on three objective functions (“low”, “base”, and “high” simulation scenarios) is developed and scalarised using the weighted sum method to find the optimised compromise between energy use for different scenarios. Necessitated by the increased computational demand of multi-objective problems, a modified version of the Ant Colony Optimisation algorithm for Mixed Variables (ACOMV-M) is developed. A comparison between ACOMV-M and a benchmark algorithm showed that ACOMV-M converged to solutions of similar quality with approximately 50% fewer simulations. The results on an Australian office building showed that the energy-optimised building parameters can vary significantly for different assumptions. Furthermore, inaccurate assumptions on internal loads and infiltration rate can reduce energy savings achieved by optimisation up to 4.8 percentage points. The proposed methodology is used to identify parameters that are sensitive to different scenarios and demonstrated that more robust solutions can be achieved through modest sacrifices in optimality to any one scenario. |
Keywords | Building optimisation, Ant colony optimisation, Uncertainty Analysis, Robust optimised design, Australian commercial building |
ANZSRC Field of Research 2020 | 339999. Other built environment and design not elsewhere classified |
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/q5x22/building-energy-optimisation-under-uncertainty-using-acomv-algorithm
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