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

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

Energy, 2025, vol. 328, issue C

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.

Keywords: Photovoltaic facades; Prefabricated academic buildings; Transfer learning; Genetic algorithms; Carbon emissions; Daylighting and thermal comfort (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225021127
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:328:y:2025:i:c:s0360544225021127

DOI: 10.1016/j.energy.2025.136470

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().