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Development of an engineering-friendly evaluation model for solar spectral irradiance using readily accessible subaerial meteorology

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  • Wang, Shuhao
  • Peng, Jinqing
  • Luo, Yimo
  • Ma, Tao
  • Xue, Peng
  • Wu, Yupeng
  • Zhang, Qiangzhi
  • Zhou, Jiayu

Abstract

Solar spectral irradiance has a crucial impact on building energy conservation, especially on photovoltaic (PV) generation. However, it takes a high cost to measure and predict the dynamic solar spectral irradiance for various atmosphere conditions and sun positions. Combining with machine learning, this paper developed a novel solar spectral irradiance estimation model to evaluate the annual solar spectral property in a region. This paper employs the readily accessible subaerial meteorology as model input. The average photon energy (APE) serves as a connection between the normalized solar spectral irradiance and the meteorology parameters. Verification showed the model this paper proposed estimated the normalized solar spectral irradiance well. Further, annual simulation of solar spectral irradiance was conducted by inputting typical meteorology year (TMY) dataset. The annual difference of the normalized spectral irradiance reached to 10.57 %, which reflects the great importance to determine the practical solar spectral irradiance. A typical day of spectra was proposed for each month to reveal the monthly variation in solar spectral irradiance. This study provides a convenient technical method to evaluate the solar spectral property for engineering applications. The results may guide industries in selecting suitable solar cells for the region, thereby prompting the development of solar applications.

Suggested Citation

  • Wang, Shuhao & Peng, Jinqing & Luo, Yimo & Ma, Tao & Xue, Peng & Wu, Yupeng & Zhang, Qiangzhi & Zhou, Jiayu, 2024. "Development of an engineering-friendly evaluation model for solar spectral irradiance using readily accessible subaerial meteorology," Renewable Energy, Elsevier, vol. 237(PA).
    • Handle: RePEc:eee:renene:v:237:y:2024:i:pa:s0960148124017002
    DOI: 10.1016/j.renene.2024.121632
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    References listed on IDEAS

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    1. Fouilloy, Alexis & Voyant, Cyril & Notton, Gilles & Motte, Fabrice & Paoli, Christophe & Nivet, Marie-Laure & Guillot, Emmanuel & Duchaud, Jean-Laurent, 2018. "Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability," Energy, Elsevier, vol. 165(PA), pages 620-629.
    2. Wang, Shuhao & Peng, Jinqing & Wang, Meng & Xue, Peng & Luo, Yimo & Ma, Tao & Zhao, Yifan, 2023. "Evaluation of the energy conversion performance of different photovoltaic materials with measured solar spectral irradiance," Renewable Energy, Elsevier, vol. 219(P1).
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    1. 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).

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