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Surface temperature and power generation efficiency of PV arrays with various row spacings: A full-scale outdoor experimental study

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  • Huang, Lin
  • Song, Zihao
  • Dong, Qichang
  • Song, Ye
  • Zhao, Xiaoqing
  • Qi, Jiacheng
  • Shi, Long

Abstract

Quantifying the relationship between surface temperature and power generation efficiency of solar photovoltaics (PV) is critical to their practical implementation. Although empirical models have been developed on this, they were mainly based on indoor laboratory tests, ignoring a practically significant arrangement factor. Therefore, a combined outdoor experimental and empirical study on PV array systems considering various row spacings was undertaken through this study. Experimental results indicated that solar irradiance shows a relatively more prominent effect than row spacing and wind speed in outdoor environments. During the practical arrangement, it is only necessary to ensure that there is no shadow between the adjacent rows of PV arrays in practical applications, and the possible cooling effect brought by excessive row spacing may not be pursued. However, this does not mean that row spacing can be ignored when predicting surface temperature and power generation efficiency. Based on the data from our long-term experimental tests, empirical models to predict solar PV's surface temperature and power generation efficiency were developed, considering various row spacings. Comparison showed that the models developed in this study can reduce the mean relative error of the previous model's predictions from 20% to 5%, which proves the necessity of including row spacing in the models. This study's developed models and research outcomes pave the way for future large-scale practical analysis of solar PV arrays.

Suggested Citation

  • Huang, Lin & Song, Zihao & Dong, Qichang & Song, Ye & Zhao, Xiaoqing & Qi, Jiacheng & Shi, Long, 2024. "Surface temperature and power generation efficiency of PV arrays with various row spacings: A full-scale outdoor experimental study," Applied Energy, Elsevier, vol. 367(C).
    • Handle: RePEc:eee:appene:v:367:y:2024:i:c:s0306261924007451
    DOI: 10.1016/j.apenergy.2024.123362
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    1. Song, Ye & Huang, Lin & Wang, Yong & Du, Yaohan & Song, Zihao & Dong, Qichang & Zhao, Xiaoqing & Qi, Jiacheng & Zhang, Guomin & Li, Wengui & Shi, Long, 2025. "Energy performance and fire risk of solar PV panels under partial shading: An experimental study," Renewable Energy, Elsevier, vol. 246(C).
    2. Gao, Yuan & Hu, Zehuan & Chen, Wei-An & Liu, Mingzhe & Ruan, Yingjun, 2025. "A revolutionary neural network architecture with interpretability and flexibility based on Kolmogorov–Arnold for solar radiation and temperature forecasting," Applied Energy, Elsevier, vol. 378(PA).
    3. Weiping Zhao & Shuai Hu & Zhiguang Dong, 2025. "Impact of Multiple Factors on Temperature Distribution and Output Performance in Dusty Photovoltaic Modules: Implications for Sustainable Solar Energy," Energies, MDPI, vol. 18(13), pages 1-17, June.
    4. Sang, Wenhu & Ma, Yuxin & Li, Senyuan & Xue, Peng & Li, Bojia & Peng, Jinqing & Fan, Man, 2024. "Spectral correction of photovoltaic module electrical properties," Renewable Energy, Elsevier, vol. 237(PD).
    5. Lee, Sihwan & Ito, Risa, 2025. "Development and verification of an airflow-type photovoltaic-integrated shading device on building façades," Applied Energy, Elsevier, vol. 383(C).
    6. Zhao, Zilong & Lv, Guoquan & Xu, Yanwen, 2025. "Facilitate the design of residential PV using reliability-based design optimization," Renewable Energy, Elsevier, vol. 240(C).

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