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Applying multi-objective genetic algorithms in green building design optimization
Abstract
Abstract Since buildings have considerable impacts on the environment, it has become necessary to pay more attention to environmental performance in building design. However, it is a difficult task to find better design alternatives satisfying several conflicting criteria, especially, economical and environmental performance. This paper presents a multi-objective optimization model that could assist designers in green building design. Variables in the model include those parameters that are usually determined at the conceptual design stage and that have critical influence on building performance. Life cycle analysis methodology is employed to evaluate design alternatives for both economical and environmental criteria. Life cycle environmental impacts are evaluated in terms of expanded cumulative exergy consumption, which is the sum of exergy consumption due to resource inputs and abatement exergy required to recover the negative impacts due to waste emissions. A multi-objective genetic algorithm is employed to find optimal solutions. A case study is presented and the effectiveness of the approach is demonstrated for identifying a number of Pareto optimal solutions for green building design.
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Cited by
A review on simulation-based optimization methods applied to building performance analysis
Nguyen, A.-T., Reiter, S., & Rigo, P. (2014) · Applied Energy · doi:10.1016/j.apenergy.2013.08.061 · Cited by 1,379
Ten Questions Concerning Model Predictive Control for Energy Efficient Buildings
Killian, M., & Kozek, M. (2016) · Building and Environment · doi:10.1016/j.buildenv.2016.05.034 · Cited by 463
Miah, J. H., Koh, S. C. L., & Stone, D. (2017) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2017.08.187 · Cited by 160
Lan, K., Kelley, S. S., Nepal, P., & Yao, Y. (2020) · Environmental Research Letters · doi:10.1088/1748-9326/abc5e6 · Cited by 95
Murray, S. N., Walsh, B. P., Kelliher, D., & O'Sullivan, D. T. J. (2014) · Building and Environment · doi:10.1016/j.buildenv.2014.01.011 · Cited by 93
Metadata
- Authors
- Wang, W., Zmeureanu, R., & Rivard, H.
- Venue
- Building and Environment
- Publisher
- Elsevier BV
- Published
- 2005-11
- Volume
- 40
- Issue
- 11
- Pages
- 1512-1525
- ISSN
- 0360-1323 (ISSN-L)
- OpenAlex
- W2041243828
- Cited by
- 726
- References
- 0
- FWCI
- 36.60
- Type
- journal article
- Language
- en
- Open access
- closed
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- Wang, W., Zmeureanu, R., & Rivard, H. (2005). Applying multi-objective genetic algorithms in green building design optimization. Building and Environment. https://doi.org/10.1016/j.buildenv.2004.11.017
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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