Integrating Gradient Boosting and Parametric Architecture for Optimizing Energy Use Intensity in Net-Zero Energy Buildings

Maqbul Kamaruddin, Martin C. T. Manullang, Jurng-Jae Yee

Abstract


Achieving net-zero energy building (NZEB) status requires accurate energy use intensity (EUI) calculations, as conventional methods often fail to capture the complexity of design and climatic conditions. In this research, a parametric energy modeling approach was used to conduct 1,350 simulations and analyze the impact of design parameters on building EUI. These simulations covered six building types—an existing building and I-, L-, T-, U-, and H-shaped buildings—across eight locations in different climate zones. A case study was conducted in Busan, Korea, where on-site measurements were obtained using portable devices to validate the simulation results. I-shaped buildings exhibited the lowest EUI, reaching 109 kWh/m²/yr at 0° and 180° orientations. The simulation results indicated that building orientations of 140°, 90°, 135°, and 270° tended to produce higher EUI values, whereas 0° and 180° showed lower EUI values of 122 and 123 kWh/m²/yr, respectively. The use of triple-pane insulated glass effectively reduced the I-shaped building's EUI to 103 kWh/m²/yr. Implementing photovoltaic (PV) systems further reduced the EUI significantly, with the I-shaped building achieving an EUI of −14 kWh/m²/yr at a 20% PV efficiency. Analysis using an extreme gradient boosting (XGBoost) model revealed that the climate zone, PV area, and type of heating, ventilation, and air-conditioning system significantly affected the EUI. This model, processed using Colab, was highly effective, with an R-squared value of 0.99, a root mean square error of 4.57, and a mean absolute error of 1.99. These findings demonstrate that the XGBoost model can effectively capture complex data patterns and can be used for high-accuracy EUI estimation.

 

Doi: 10.28991/CEJ-2025-011-03-06

Full Text: PDF


Keywords


Gradient Boosting; Parametric Architecture; Energy Use Intensity; Net-Zero Energy Buildings.

References


Almomani, A., Almeida, R. M. S. F., Vicente, R., & Barreira, E. (2024). Critical Review on the Energy Retrofitting Trends in Residential Buildings of Arab Mashreq and Maghreb Countries. Buildings, 14(2), 338. doi:10.3390/buildings14020338.

Mehedhi, H., AMM, S. A., Mohammad Alamgir, H., & AMM, N. A. (2024). The prospects of zero energy building as an alternative to the conventional building system in Bangladesh (A review). Journal of Civil Engineering and Environmental Sciences, 10(2), 039–049. doi:10.17352/2455-488x.000082.

Noh, Y., Jafarinejad, S., & Anand, P. (2024). A Review on Harnessing Renewable Energy Synergies for Achieving Urban Net-Zero Energy Buildings: Technologies, Performance Evaluation, Policies, Challenges, and Future Direction. Sustainability (Switzerland), 16(8). doi:10.3390/su16083444.

Xiaoxiang, Q., Junjia, Y., Haron, N. A., Alias, A. H., Law, T. H., & Abu Bakar, N. (2024). Status, Challenges and Future Directions in the Evaluation of Net-Zero Energy Building Retrofits: A Bibliometrics-Based Systematic Review. Energies, 17(15), 3826. doi:10.3390/en17153826.

Hongvityakorn, B., Jaruwasupant, N., Khongphinitbunjong, K., & Aggarangsi, P. (2024). Achieving Nearly Zero-Energy Buildings through Renewable Energy Production-Storage Optimization. Energies, 17(19), 4845. doi:10.3390/en17194845.

Panchal, P., Mehta, V., Thakur, M., & Kumar, B. (2020). Pathway to Net-Zero Energy Buildings. Plant Archives, 20 Special (August), 156–159.

Marszal, A. J., & Heiselberg, P. (2011). Life cycle cost analysis of a multi-storey residential Net Zero Energy Building in Denmark. Energy, 36(9), 5600–5609. doi:10.1016/j.energy.2011.07.010.

Chen, S. Y. (2019). Use of green building information modeling in the assessment of net zero energy building design. Journal of Environmental Engineering and Landscape Management, 27(3), 174–186. doi:10.3846/jeelm.2019.10797.

Becchio, C., Corgnati, S. P., Vio, M., Crespi, G., Prendin, L., Ranieri, M., & Vidotto, D. (2017). Toward NZEB by optimizing HVAC system configuration in different climates. Energy Procedia, 140, 115–126. doi:10.1016/j.egypro.2017.11.128.

Latief, Y., Berawi, M. A., Koesalamwardi, A. B., Sagita, L., & Herzanita, A. (2019). Cost optimum design of a tropical near zero energy house (nZEH). International Journal of Technology, 10(2), 376–385. doi:10.14716/ijtech.v10i2.1781.

Moreno, B., Del Ama Gonzalo, F., Ferrandiz, J. A., Lauret, B., & Hernandez, J. A. (2019). A building energy simulation methodology to validate energy balance and comfort in zero energy buildings. Journal of Energy Systems, 3(4), 168–182. doi:10.30521/jes.623285.

Santos-Herrero, J. M., Lopez-Guede, J. M., & Flores-Abascal, I. (2021). Modeling, simulation and control tools for nZEB: A state-of-the-art review. Renewable and Sustainable Energy Reviews, 142. doi:10.1016/j.rser.2021.110851.

Mahiwal, S. G., Bhoi, M. K., & Bhatt, N. (2021). Evaluation of energy use intensity (EUI) and energy cost of commercial building in India using BIM technology. Asian Journal of Civil Engineering, 22(5), 877–894. doi:10.1007/s42107-021-00352-5.

Geng, Y., Lin, B., & Zhu, Y. (2020). Comparative study on indoor environmental quality of green office buildings with different levels of energy use intensity. Building and Environment, 168(August), 106482. doi:10.1016/j.buildenv.2019.106482.

Samadi, M., & Fattahi, J. (2021). Energy use intensity disaggregation in institutional buildings – A data analytics approach. Energy and Buildings, 235. doi:10.1016/j.enbuild.2021.110730.

Raimo Simson, Kirsten Engelund Thomsen, Kim Bjarne Wittchen, & Jarek Kurnitski. (2021). NZEB Requirements vs European Benchmarks in Residential Buildings. The REHVA European HVAC Journal, 58(2), 40–44.

Garcia, J. F., & Kranzl, L. (2018). Ambition levels of nearly zero energy buildings (nZEB) definitions: An approach for cross-country comparison. Buildings, 8(10), 143. doi:10.3390/buildings8100143.

Tabrizi, A. (2021). Sustainable Construction, LEED as a Green Rating System and the Importance of Moving to NZEB. E3S Web of Conferences, 241. doi:10.1051/e3sconf/202124102001.

USGBC. (2025). LEED V4.1 Building Design and Construction. U.S. Green Building Council (USGBC), Washington, United States. Available online: https://build.usgbc.org/bd+c_guide (accessed on February 2025).

Xia, B., Zuo, J., Skitmore, M., Pullen, S., & Chen, Q. (2013). Green Star Points Obtained by Australian Building Projects. Journal of Architectural Engineering, 19(4), 302–308. doi:10.1061/(asce)ae.1943-5568.0000121.

DOEE. (2018). Green Building Report 2018. DC. Department of Energy & Environment (DOEE), Washington, United States. Available online: https://www.slideshare.net/slideshow/2018-green-building-report-finalpptx/254542535#27 (accessed on February 2025).

Tracy, W. H., Joy, S., Flora, Y., & Audrey, W. (2020). Green Building Rating Systems: Energy Benchmarking Study. Environment Design Guide, Civic Exchange, Hong Kong.

Grainger, C., & Rattenbury, J. (2021). Net Zero New Buildings Evidence and guidance to inform Planning Policy. South West Energy Hub, Bristol, United Kingdom.

IBEC. (2014). Japan Sustainable Building Consortium, CASBEE for Buildings (New Construction) (1st Ed.). Volume 1, Institute for Building Environment and Energy Conservation (IBEC), Tokyo, Japan.

Portalatin, M., Roskoski, M., & Shouse, T. (2015). Sustainability How-to Guide Series Green Building Rating Systems. Houston, United States. Available online: http://cdn.ifma.org/sfcdn/membership-documents/green-rating-systems-htg-final.pdf (accessed on February 2025).

Wang, N., Fowler, K. M., & Sullivan, R. S. (2012). Green Building Certification System. PNNL-20966, US Department of Energy, Washington, United States.

Robar, K. (2018). Comparative analysis study to compare LEED v4 and green globes in Newfoundland and Labrador. Morrison Hershfield Limited, Ottawa, Canada.

Vierra, S. (2018). Green Building Standards and Certification Systems. Whole Building Design Guide (WBDG), National Institute for Building Sciences, Washington, United States. Available online: https://globalgbc.org/wp-content/uploads/2022/07/034_green-building-standards-and-certification-system.pdf (accessed on February 2025).

Lee, J., & Shepley, M. (2019). The green standard for energy and environmental design (g-seed) for multi-family housing rating system in Korea: A review of evaluating practices and suggestions for improvement. Journal of Green Building, 14(2), 155–176. doi:10.3992/1943-4618.14.2.155.

Kim, K. H., Chae, C. U., & Cho, D. (2022). Development of an assessment method for energy performance of residential buildings using G-SEED in South Korea. Journal of Asian Architecture and Building Engineering, 21(1), 133–144. doi:10.1080/13467581.2020.1838286.

No, S., & Won, C. (2020). Comparative analysis of energy consumption between green building certified and non-certified buildings in Korea. Energies, 13(5), 1049. doi:10.3390/en13051049.

Nahan, R. T. (2019). Architect’s Guide to Building Performance: Integrating performance simulation in the design process. The American Institute of Architects, New York, United States.

Suphavarophas, P., Wongmahasiri, R., Keonil, N., & Bunyarittikit, S. (2024). A Systematic Review of Applications of Generative Design Methods for Energy Efficiency in Buildings. Buildings, 14(5), 1311. doi:10.3390/buildings14051311.

Stevanović, S., Dashti, H., Milošević, M., Al-Yakoob, S., & Stevanović, D. (2024). Comparison of ANN and XGBoost surrogate models trained on small numbers of building energy simulations. PloS One, 19(10), e0312573. doi:10.1371/journal.pone.0312573.

Lu, Y., Wu, W., Geng, X., Liu, Y., Zheng, H., & Hou, M. (2022). Multi-Objective Optimization of Building Environmental Performance: An Integrated Parametric Design Method Based on Machine Learning Approaches. Energies, 15(19), 7031. doi:10.3390/en15197031.

Labib, R. (2022). Integrating Machine Learning with Parametric Modeling Environments to Predict Building Daylighting Performance. IOP Conference Series: Earth and Environmental Science, 1085(1), 8DUMMY. doi:10.1088/1755-1315/1085/1/012006.

Veiga, R. K., Veloso, A. C., Melo, A. P., & Lamberts, R. (2021). Application of machine learning to estimate building energy use intensities. Energy and Buildings, 249, 111219. doi:10.1016/j.enbuild.2021.111219.

Seyedzadeh, S., Pour Rahimian, F., Rastogi, P., & Glesk, I. (2019). Tuning machine learning models for prediction of building energy loads. Sustainable Cities and Society, 47. doi:10.1016/j.scs.2019.101484.

Amasyali, K., & El-Gohary, N. (2021). Machine learning for occupant-behavior-sensitive cooling energy consumption prediction in office buildings. Renewable and Sustainable Energy Reviews, 142. doi:10.1016/j.rser.2021.110714.

Solmaz, A. S. (2020). Machine learning based optimization approach for building energy performance. 2020 Building Performance Analysis Conference and SimBuild, 29 September-1 October, Virtual Meeting.

Guo, H., Duan, D., Yan, J., Ding, K., Xiang, F., & Peng, R. (2022). Machine Learning-Based Method for Detached Energy-Saving Residential Form Generation. Buildings, 12(10), 1504. doi:10.3390/buildings12101504.

Barbaresi, A., Ceccarelli, M., Menichetti, G., Torreggiani, D., Tassinari, P., & Bovo, M. (2022). Application of Machine Learning Models for Fast and Accurate Predictions of Building Energy Need. Energies, 15(4), 1266. doi:10.3390/en15041266.

Kumar, P., Kamalakshi, N., & Karthick, T. (2024). Towards Sustainable Architecture: Machine Learning for Predicting Energy Use in Buildings. Proceedings 3rd International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2024, 10602461. doi:10.1109/ACCAI61061.2024.10602461.

Ni, Z., Zhang, C., Karlsson, M., & Gong, S. (2023). Leveraging Deep Learning and Digital Twins to Improve Energy Performance of Buildings. 2023 IEEE 3rd International Conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023, Shanghai, China. doi:10.1109/IESES53571.2023.10253721.

Gan, H., & Gao, W. (2024). Ensemble machine learning for managing the required thermal energy from the architectural characteristics of residential buildings. International Journal of Low-Carbon Technologies, 19, 1222–1230. doi:10.1093/ijlct/ctae064.

Abdelsattar, M., Ismeil, M. A., Azim Zayed, M. M. A., Abdelmoety, A., & Emad-Eldeen, A. (2024). Assessing Machine Learning Approaches for Photovoltaic Energy Prediction in Sustainable Energy Systems. IEEE Access, 12, 107599–107615. doi:10.1109/ACCESS.2024.3437191.

Barbur, V. A., Montgomery, D. C., & Peck, E. A. (1994). Introduction to Linear Regression Analysis. The Statistician, 43(2), 339. doi:10.2307/2348362.

Chen, T., & Guestrin, C. (2016). XGBoost. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. doi:10.1145/2939672.2939785.

Breiman, L. (2001). Machine Learning, 45(1), 5–32. doi:10.1023/a:1010933404324.

Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. doi:10.1214/aos/1013203451.

Smola, A. J., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14(3), 199–222. doi:10.1023/b:stco.0000035301.49549.8.


Full Text: PDF

DOI: 10.28991/CEJ-2025-011-03-06

Refbacks

  • There are currently no refbacks.




Copyright (c) 2025 Maqbul Kamaruddin, Martin Clinton Tosima Manullang, Jurng-Jae Yee

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
x
Message