Vol. 15 No. 05 (2025): Journal of Materials & Construction
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Tạp chí Vật liệu & Xây dựng - Bộ Xây dựng

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Prediction and performance evaluation of building-integrated photovoltaic facades using artificial intelligence models

Trần Đức Học , Hoàng Hải Triều , Nguyễn Ngọc Thoan , Hồ Ngọc Khoa

Keywords:

BIPV
Energy saving
Prediction
Artificial intelligence

Abstract

The continuous growth of global energy consumption due to industrialization and urban development has significantly contributed to climate change. In response, renewable energy, particularly solar energy, has emerged as a key solution toward green and sustainable building design. This study proposes the application of artificial intelligence algorithms, including Gradient boosting (GB), Extreme gradient boosting (XGBoost), Light gradient boosting machine (LightGBM), and Categorical boosting (CatBoost), to develop predictive models for building energy consumption and photovoltaic electricity generation in office buildings equipped with glass-based building-integrated photovoltaics (BIPV) facades. Input data were generated through simulation using DesignBuilder software. Among the models, CatBoost yielded the best performance, with R² values of 0.972 for energy consumption and 0.974 for electricity generation. Furthermore, the integration of BIPV systems into building facades has the potential to reduce energy consumption by up to 34.3%, highlighting its practical benefits for energy-efficient building design.

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How to Cite

Trần Đức Học, Hoàng Hải Triều, Nguyễn Ngọc Thoan, & Hồ Ngọc Khoa. (2025). Prediction and performance evaluation of building-integrated photovoltaic facades using artificial intelligence models. Tạp Chí Vật liệu & Xây dựng - Bộ Xây dựng, 15(05), Trang 73 – Trang 80. https://doi.org/10.54772/jomc.05.2025.1143

References

  1. J. Davenport and N. Wayth, "Statistical review of world energy," Energy institute, 2023.
  2. H. T. Hoi, "Potential for solar energy development in Vietnam," International Journal of Environmental Science and Development, vol. 11, no. 7, pp. 358-364, 2020.
  3. M. Roginska-Niesluchowska, "Use of daylight and aesthetic image of glass facades in contemporary buildings," in IOP Conference Series: Materials Science and Engineering, 2017, vol. 245, no. 8, p. 082035: IOP Publishing.
  4. M. J. Perez, V. Fthenakis, H. C. Kim, and A. O. Pereira, "Façade–integrated photovoltaics: a life cycle and performance assessment case study," Progress in Photovoltaics: Research and Applications, vol. 20, no. 8, pp. 975-990, 2012.
  5. A. Ghazali, L. C. Haw, S. Mat, and K. Sopian, "Performance and financial evaluation of various photovoltaic vertical facades on high-rise building in Malaysia," Energy and Buildings, vol. 134, pp. 306-318, 2017.
  6. H. Gholami, H. N. Røstvik, N. M. Kumar, and S. S. Chopra, "Lifecycle cost analysis (LCCA) of tailor-made building integrated photovoltaics (BIPV) façade: Solsmaragden case study in Norway," Solar Energy, vol. 211, pp. 488-502, 2020.
  7. Q. T. Nguyen, D. L. Luong, A. D. Pham, and Q. C. Truong, "Developing an optimization model of solar cell installation on building façades in high-rise buildings–A case study in Viet Nam," GMSARN Int. J, vol. 15, no. 1, pp. 44-49, 2021.
  8. S. Attia, E. Gratia, A. De Herde, and J. L. Hensen, "Simulation-based decision support tool for early stages of zero-energy building design," Energy and buildings, vol. 49, pp. 2-15, 2012.
  9. R. Kabilan et al., "Short‐Term Power Prediction of Building Integrated Photovoltaic (BIPV) System Based on Machine Learning Algorithms," International Journal of Photoenergy, vol. 2021, no. 1, p. 5582418, 2021.
  10. S. T. Imalka, R. J. Yang, and Y. Zhao, "Machine learning driven building integrated photovoltaic (BIPV) envelope design optimization," Energy and Buildings, vol. 324, p. 114882, 2024.
  11. T. Đ. Học, "Tối ưu hóa ước tính mức tiêu thụ năng lượng trong các tòa nhà dựa trên các thuật toán trí tuệ nhân tạo," Tạp chí Khoa học Công nghệ Xây dựng (TCKHCNXD)-ĐHXDHN, vol. 14, no. 1V, pp. 35-45, 2020.
  12. L. D. Long, "An AI-driven model for predicting and optimizing energy-efficient building envelopes," Alexandria Engineering Journal, vol. 79, pp. 480-501, 2023.
  13. J. H. Friedman, "Greedy function approximation: a gradient boosting machine," Annals of statistics, pp. 1189-1232, 2001.
  14. J. H. Friedman, "Stochastic gradient boosting," Computational statistics & data analysis, vol. 38, no. 4, pp. 367-378, 2002.
  15. T. Chen and C. Guestrin, "Xgboost: A scalable tree boosting system," in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 2016, pp. 785-794.
  16. G. Ke et al., "Lightgbm: A highly efficient gradient boosting decision tree," Advances in neural information processing systems, vol. 30, 2017.
  17. L. Prokhorenkova, G. Gusev, A. Vorobev, A. V. Dorogush, and A. Gulin, "CatBoost: unbiased boosting with categorical features," Advances in neural information processing systems, vol. 31, 2018.