A methodological approach to energy optimization for buildings by utilizing machine learning-based surrogate model and an optimization technique

Issue 16(1)2026
Published May 30, 2026
https://doi.org/10.46223/HCMCOUJS.acs.en.16.1.84.2026
Date
Received: 2026-03-17
Accepted: 2026-05-19
Published: 2026-05-30
Download
PDF Appendix
Statistics
Views: 153
PDF - Downloads: 5 Appendix - Downloads: 0

Downloads

Download data is not yet available.
16(1)2026

##plugins.themes.academic_pro.article.main##

Le Quang Huy - 3035565L@student.gla.ac.uk
Glasgow Computational Engineering Centre, James Watt School of Engineering, University of Glasgow
https://orcid.org/0000-0001-7234-1187
Hoang Quynh Anh
Ho Chi Minh City University of Transport, Ho Chi Minh City
https://orcid.org/0000-0003-4570-5189

Abstract

This study presents an application procedure for architectural design of residential buildings in Vietnam, with the objective of reducing environmental effects. Particularly, this practical procedure utilizes software to simulate the energy usage of the building and then applies a machine learning model to build an energy surrogate model. The applicability of the surrogate model is guaranteed by its high accuracy, with the relative error of only around 1.5%, as well as by a high correlation of greater than 0.9 between the predictions and true values. An optimization framework is formulated to identify an optimal architectural design by minimizing the building's energy requirements through an objective function based on the surrogate model and subject to architectural constraints aligned with local design principles. The optimization process is executed using the Constriction Particle Swarm Optimization algorithm. The algorithm shows fast convergence, which is within 40 iterations. The presented procedure is illustrated through a case study of a residential building located in An Giang province, Vietnam, utilizing the most recent climate data officially published by the Vietnamese Government. The results demonstrate the methodology’s potential for practical design applications, with key discussions provided on the findings and future research directions.

Keywords

architectural optimization; building energy simulation (BEM); building’s energy simulations; constriction particle swarm optimization; machine learning-based surrogate modeling

License
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite
Le, Q. H., & Hoang, Q. A. (2026). A methodological approach to energy optimization for buildings by utilizing machine learning-based surrogate model and an optimization technique. HCMCOU Journal of Science - Advances in Computational Structures, 16(1), 30–60. https://doi.org/10.46223/HCMCOUJS.acs.en.16.1.84.2026
Crossref
Scopus
Google Scholar
Europe PMC

References

  1. Alanne, K., & Sierla, S. (2022). An overview of machine learning applications for smart buildings. Sustainable Cities and Society, 76, Article 103445.
  2. Boutahri, Y., & Tilioua, A. (2024). Machine learning-based predictive model for thermal comfort and energy optimization in smart buildings. Results in Engineering, 22, Article 102148.
  3. Cao, Y., Nakhjiri, A. T., & Ghadiri, M. (2024). Different applications of machine learning approaches in materials science and engineering: Comprehensive review. Engineering Applications of Artificial Intelligence, 135, Article 108783.
  4. Chaabene, W. B., Flah, M. L., & Nehru, M. L. (2020). Machine learning prediction of mechanical properties of concrete: Critical review. Construction and Building Materials, 260, Article 119889.
  5. Clerc, M., & Kennedy, J. (2002). The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation, 6(1), 58-73.
  6. Egwim, C. N., Alaka, H., Egunjobi, O. O., Gomes, A., & Mporas, I. (2024). Comparison of machine learning algorithms for evaluating building energy efficiency using big data analytics. Journal of Engineering, Design and Technology, 22(4), 1325-1350.
  7. Fathi, S., Srinivasan, R., Fenner, A., & Fathi, S. (2020). Machine learning applications in urban building energy performance forecasting: A systematic review. Renewable and Sustainable Energy Reviews, 133, Article 110287.
  8. García-Martín, E., Rodrigues, C. F., Riley, G., & Grahn, H. (2019). Estimation of energy consumption in machine learning. Journal of Parallel and Distributed Computing, 134, 75-88.
  9. Garzón, A., Kapelan, Z., Langeveld, J., & Taormina, R. (2022). Machine learning-based surrogate modelling for urban water networks: Review and future research directions. Water Resources Research, 58(5), e2021WR031808.
  10. General Statistics Office (GSO). (2022). Statistical yearbook of Vietnam 2022. https://www.nso.gov.vn/en/data-and-statistics/2023/06/statistical-yearbook-of-2022/
  11. Hu, F., & Zheng, X. (2015). Carbon emission of energy efficient residential building. Procedia Engineering, 121, 1096-1102.
  12. Intergovernmental Panel on Climate Change. (2023). Sections. In Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. https://www.ipcc.ch/report/ar6/syr/downloads/report/IPCC_AR6_SYR_LongerReport.pdf
  13. International Energy Agency & United Nations Environment Programme. (2018). 2018 global status report: Toward a zero-emission, efficient and resilient buildings and construction sector (Tech. Rep.). International Energy Agency.
  14. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN’95 - International Conference on Neural Networks, 4, 1942-1948.
  15. Kingma, D. P. (2014). Adam: A method for stochastic optimization. arXiv. https://arxiv.org/abs/1412.6980
  16. Kofoworola, O. F., & Gheewala, S. H. (2008). Environmental life cycle assessment of a commercial office building in Thailand. The International Journal of Life Cycle Assessment, 13(6), 498-511.
  17. Luo, J., Ma, X., Ji, Y., Li, X., Song, Z., & Lu, W. (2023). Review of machine learning-based surrogate models of groundwater contaminant modeling. Environmental Research, 238(Part 2), Article 117268. https://doi.org/10.1016/j.envres.2023.117268
  18. McBride, K., & Sundmacher, K. (2019). Overview of surrogate modeling in chemical process engineering. Chemie Ingenieur Technik, 91(3), 228-239.
  19. Mostafavi, F., Tahsildoost, M., & Zomorodian, Z. (2021). Energy efficiency and carbon emission in high-rise buildings: A review (2005-2020). Building and Environment, 206, Article 108329.
  20. Olu-Ajayi, R., Alaka, H., Sulaimon, I., Sunmola, F., & Ajayi, S. (2022). Machine learning for energy performance prediction at the design stage of buildings. Energy for Sustainable Development, 66, 12-25.
  21. 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), 1036-1048.
  22. Prina, M. G., Dallapiccola, M., Moser, D., & Sparber, W. (2024). Machine learning as a surrogate model for energy plan: Speeding up energy system optimization at the country level. Energy, 307, Article 132735.
  23. Robinson, C., Dilkina, B., Hubbs, J., Zhang, W., Guhathakurta, S., Brown, M. A., & Pendyala, R. M. (2017). Machine learning approaches for estimating commercial building energy consumption. Applied Energy, 208, 889-904.
  24. Seyedzadeh, S., Rahimian, F. P., & Glesk, I. (2019). Tuning machine learning models for prediction of building energy loads. Sustainable Cities and Society, 47, Article 101484.
  25. Seyedzadeh, S., Rahimian, F. P., Glesk, I., & Roper, M. (2018). Machine learning for estimation of building energy consumption and performance: A review. Visualization in Engineering, 6(1), 1-20.
  26. Singaravel, S., Geyer, P., & Suykens, J. (2017). Component-based machine learning modelling approach for design stage building energy prediction: Weather conditions and size. In C. S. Barnaby & M. Wetter (Eds.), Proceedings of Building Simulation 2017: 15th Conference of IBPSA (Vol. 15, pp. 212-221). International Building Performance Simulation Association.
  27. Sun, G., & Wang, S. (2019). A review of the artificial neural network surrogate modeling in aerodynamic design. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 233(16), 5863-5872.
  28. United Nations Environment Programme. (2009). Buildings and climate change: Summary for decision makers (Tech. Rep.).
  29. World Bank. (2019). Climate data-projections (Tech. Rep.). Climate Change Knowledge Portal. https://climateknowledgeportal.worldbank.org/country/vietnam/climate-data-projections
  30. World Meteorological Organization (WMO). (2023). Provisional state of the global climate in 2023. https://wmo.int/publication-series/provisional-state-of-global-climate-2023
  31. Wu, X., Feng, Z., Chen, H., Qin, Y., Zheng, S., Wang, L., Liu, Y., & Skibniewski, M. J. (2022). Intelligent optimization framework of near zero energy consumption building performance based on a hybrid machine learning algorithm. Renewable and Sustainable Energy Reviews, 167, Article 112703.
  32. Yang, S., Wan, M. P., Chen, W., Ng, B. F., & Dubey, S. (2020). Model predictive control with adaptive machine learning-based model for building energy efficiency and comfort optimization. Applied Energy, 271, Article 115147.
  33. Zhong, S., Zhang, K., Bagheri, M., Burken, J. G., Gu, A., Li, B., Ma, X., Marrone, L. L., Ren, Z. J., Schrier, J., Shi, W., Tan, H., Wang, T.,Wang, X., Wong, B. M., Xiao, X., Yu, X., Zhu, J.-J., & Zhang, H. (2021). Machine learning: New ideas and tools in environmental science and engineering. Environmental Science & Technology, 55(19), 12741-12754.

Similar Articles

1 2 > >> 

You may also start an advanced similarity search for this article.