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Multi-objective optimization of photovoltaic facades in prefabricated academic buildings using transfer learning and genetic algorithms

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  • Chen, Zhengshu
  • Cui, Yanqiu
  • Cai, Hongbin
  • Zheng, Haichao
  • Ning, Qiao
  • Ding, Xin

Abstract

Photovoltaic (PV) facades design in academic buildings requires balancing carbon emissions, daylighting, and thermal comfort. Traditional methods often enhance indoor comfort at the expense of higher carbon emissions. Thus, this study, leveraging transfer learning and genetic algorithms, integrates building simulation, performance prediction, optimization, and CFD analysis into a multi-objective optimization workflow. Targeting net-zero carbon emissions while maintaining indoor comfort, it optimizes classroom form, enclosure performance, fenestration, and PV shading devices. The results demonstrate that: (1) carbon emissions decrease by 27.88 kgCO2/m2, daylighting improves by 1.06 %, with thermal stability; (2) PV shading devices tilt angles, window-to-wall ratios, and classroom height significantly influence building performance; (3) the integration of LGBM, CNN, and NSGA-III effectively improves the efficiency of performance predictions and optimization; (4) recommended PV panel tilt angles (0–10°) and cavity depths (60 or 150 mm) effectively reduce facade surface temperatures and improve PV module efficiency. The findings provide a scientific basis for the extensive application of PV systems on prefabricated academic building facades.

Suggested Citation

  • Chen, Zhengshu & Cui, Yanqiu & Cai, Hongbin & Zheng, Haichao & Ning, Qiao & Ding, Xin, 2025. "Multi-objective optimization of photovoltaic facades in prefabricated academic buildings using transfer learning and genetic algorithms," Energy, Elsevier, vol. 328(C).
    • Handle: RePEc:eee:energy:v:328:y:2025:i:c:s0360544225021127
    DOI: 10.1016/j.energy.2025.136470
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    References listed on IDEAS

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    1. Barbón, A. & Fortuny Ayuso, P. & Bayón, L. & Fernández-Rubiera, J.A., 2020. "Predicting beam and diffuse horizontal irradiance using Fourier expansions," Renewable Energy, Elsevier, vol. 154(C), pages 46-57.
    2. Mendis, Thushini & Huang, Zhaojian & Xu, Shen & Zhang, Weirong, 2020. "Economic potential analysis of photovoltaic integrated shading strategies on commercial building facades in urban blocks: A case study of Colombo, Sri Lanka," Energy, Elsevier, vol. 194(C).
    3. Wang, Chuyao & Ji, Jie & Yu, Bendong & Xu, Lijie & Wang, Qiliang & Tian, Xinyi, 2022. "Investigation on the operation strategy of a hybrid BIPV/T façade in plateau areas: An adaptive regulation method based on artificial neural network," Energy, Elsevier, vol. 239(PA).
    4. Khan, Waqas & Walker, Shalika & Zeiler, Wim, 2022. "Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach," Energy, Elsevier, vol. 240(C).
    5. Liu, Jiarui & Fu, Yuchen, 2023. "Renewable energy forecasting: A self-supervised learning-based transformer variant," Energy, Elsevier, vol. 284(C).
    6. Liu, Ruimiao & Liu, Zhongbing & Xiong, Wei & Zhang, Ling & Zhao, Chengliang & Yin, Yingde, 2024. "Performance simulation and optimization of building façade photovoltaic systems under different urban building layouts," Energy, Elsevier, vol. 288(C).
    7. Liu, Zhengguang & Guo, Zhiling & Chen, Qi & Song, Chenchen & Shang, Wenlong & Yuan, Meng & Zhang, Haoran, 2023. "A review of data-driven smart building-integrated photovoltaic systems: Challenges and objectives," Energy, Elsevier, vol. 263(PE).
    8. Liu, Jiang & Wu, Qifeng & Lin, Zhipeng & Shi, Huijie & Wen, Shaoyang & Wu, Qiaoyu & Zhang, Junxue & Peng, Changhai, 2023. "A novel approach for assessing rooftop-and-facade solar photovoltaic potential in rural areas using three-dimensional (3D) building models constructed with GIS," Energy, Elsevier, vol. 282(C).
    9. Ifaei, Pouya & Nazari-Heris, Morteza & Tayerani Charmchi, Amir Saman & Asadi, Somayeh & Yoo, ChangKyoo, 2023. "Sustainable energies and machine learning: An organized review of recent applications and challenges," Energy, Elsevier, vol. 266(C).
    10. Chen, Xi & Yang, Hongxing & Peng, Jinqing, 2019. "Energy optimization of high-rise commercial buildings integrated with photovoltaic facades in urban context," Energy, Elsevier, vol. 172(C), pages 1-17.
    11. Izadi, Ali & Shahafve, Masoomeh & Ahmadi, Pouria & Hanafizadeh, Pedram, 2023. "Design, and optimization of COVID-19 hospital wards to produce Oxygen and electricity through solar PV panels with hydrogen storage systems by neural network-genetic algorithm," Energy, Elsevier, vol. 263(PA).
    12. Chao Ding & Jing Ke & Mark Levine & Jessica Granderson & Nan Zhou, 2024. "Potential of artificial intelligence in reducing energy and carbon emissions of commercial buildings at scale," Nature Communications, Nature, vol. 15(1), pages 1-9, December.
    13. Liao, Wei & Xu, Shen, 2015. "Energy performance comparison among see-through amorphous-silicon PV (photovoltaic) glazings and traditional glazings under different architectural conditions in China," Energy, Elsevier, vol. 83(C), pages 267-275.
    14. Zoubin Ghahramani, 2015. "Probabilistic machine learning and artificial intelligence," Nature, Nature, vol. 521(7553), pages 452-459, May.
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