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Parameter optimization and performance evaluation of building integrated photovoltaics (BIPV) across climate zones based on a multi-node thermal model

Author

Listed:
  • Song, Xuan
  • Wang, Dengjia
  • Jiang, Hanyu
  • Wang, Yingying
  • Ma, Tao
  • Wang, Gang

Abstract

Building integrated photovoltaics (BIPV) is crucial for improving building energy efficiency and renewable energy use, with performance influenced by climate and design parameters. This study employs an experimentally validated multi-node thermal model to analyze thermoelectric behavior across varying climates and design factors. A performance evaluation system integrating power generation, thermal performance, and energy savings is developed. Parametric studies in six climate zones in China show that the heat transfer coefficient peaks at BIPV heights of 2.5∼3 m. Air channel spacings between 100 and 300 mm have significant effects, while spacings exceeding 300 mm show no substantial thermal gain. A ±40% variation in building wall heat transfer coefficient resulted in average annual-weighted heat transfer coefficient fluctuations from −57.83% to 38.57% in Harbin and −35.69% to 19.31% in Guangzhou. Increasing photovoltaic efficiency from 16% to 24% linearly enhanced power generation and reduced average annual-weighted thermal resistance enhancement coefficient by 8.16%. Multi-parameter coupling indicates that BIPV height significantly impacts annual shading gain and energy-saving efficiency, while photovoltaic efficiency has the greatest contribution to annual power gain. The building wall heat transfer coefficient influences all performance evaluations, with channel spacing having the weakest effect. The study provides quantitative guidance for optimizing BIPV design across diverse climates.

Suggested Citation

  • Song, Xuan & Wang, Dengjia & Jiang, Hanyu & Wang, Yingying & Ma, Tao & Wang, Gang, 2026. "Parameter optimization and performance evaluation of building integrated photovoltaics (BIPV) across climate zones based on a multi-node thermal model," Renewable Energy, Elsevier, vol. 262(C).
  • Handle: RePEc:eee:renene:v:262:y:2026:i:c:s0960148126002491
    DOI: 10.1016/j.renene.2026.125424
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