Predicting the surface temperature of radiative cooling coatings with time-series forecasting: Validation of the small-batch training dataset and implementation of a hyperparameter optimization strategy

Weinan Gan, Yue He, Pengbo Hu, Yunfei Fu, Yihui Yin and Chi Feng

Renewable Energy, 2025, vol. 251, issue C

Abstract: Radiative cooling coatings can maintain their surface temperature below ambient air temperature under solar radiation. The hemispherical emissivity model (HEM) is a widely used physical model for calculating the surface temperature of radiative cooling coatings; however, its reliance on fixed radiative properties limits its long-term accuracy. To solve this problem, this study used bidirectional long short-term memory (Bi-LSTM) and encoder-Transformer (E-T) models to capture the dynamic changes in the cooling performance. Subsets containing 20%–50% of the original training dataset's size were used to validate the impact of the small-batch training dataset. An automatic hyperparameter optimization strategy was also proposed to determine the optimal hyperparameter combination. The results demonstrated that across all training ratios, both the Bi-LSTM and E-T outperform HEM, with E-T providing the highest accuracy. The E-T model, within the 20%–50% training ratio range, exhibited the root mean squared error (RMSE), mean absolute error (MAE) and coefficient of determination (R2) ranging from 1.24°C to 1.58°C, 0.94°C to 1.26°C, and 0.92 to 0.95, respectively. Comparatively, the HEM showed the RMSE, MAE and R2 ranging from 1.46°C to 1.76°C, 1.18°C to 1.39°C, and 0.91 to 0.94, respectively. A roughly 15% prediction accuracy improvement was hence achieved.

Keywords: Radiative cooling coating; Time-series forecasting model; Automatic optimization strategy; Small training dataset (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:251:y:2025:i:c:s0960148125010134

DOI: 10.1016/j.renene.2025.123351

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