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Advancing photovoltaic panel temperature forecasting: A comparative study of numerical simulation and machine learning in two types of PV power plant

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  • Li, Peidu
  • Luo, Yong
  • Xia, Xin
  • Shi, Wen
  • Zheng, Junqing
  • Liao, Zhouyi
  • Gao, Xiaoqing
  • Chang, Rui

Abstract

Photovoltaic (PV) panel temperature dynamic monitoring and forecasting is important for managing and maintaining of PV power plant. However, it is uncommon to use a variety of methods to predict and evaluate the panel temperature of different types of PV power plants. Therefore, this study aims to advance PV panel temperature forecasting through a comparative analysis of numerical simulation and machine learning models in two types of PV power plants: land- and water-mounted. The results indicate that PV panel temperature condition for two types of PV power plants can be well captured by the numerical simulation (NS) and machine learning, except for the NS in water-mounted PV power plant (R2 with 0.66). Models perform better in land-mounted PV power plants, with Random Forest Regression (RFR) and ResNet models demonstrating superior accuracy across both environments. While all models exhibit some degree of deviation from normal distribution in residuals, RFR and ResNet show the least deviation and lowest prediction errors, highlighting their robustness in forecasting PV panel temperatures. This study can provide valuable insights for predicting PV panel temperatures and for the management and maintenance of PV power plants.

Suggested Citation

  • Li, Peidu & Luo, Yong & Xia, Xin & Shi, Wen & Zheng, Junqing & Liao, Zhouyi & Gao, Xiaoqing & Chang, Rui, 2024. "Advancing photovoltaic panel temperature forecasting: A comparative study of numerical simulation and machine learning in two types of PV power plant," Renewable Energy, Elsevier, vol. 237(PA).
    • Handle: RePEc:eee:renene:v:237:y:2024:i:pa:s0960148124016707
    DOI: 10.1016/j.renene.2024.121602
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    2. Bayrak, Fatih, 2025. "Prediction of photovoltaic panel cell temperatures: Application of empirical and machine learning models," Energy, Elsevier, vol. 323(C).

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