@Article{ee.2026.080474,
AUTHOR = {Jakkrawut Techo, Panupon Trairat, Karthikeyan Velmurugan},
TITLE = {Machine Learning Based Random Forest Prediction for Solar Dryer under Thailand Climatic Conditions},
JOURNAL = {Energy Engineering},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/energy/online/detail/26855},
ISSN = {1546-0118},
ABSTRACT = {In this study, selective and non-selective absorber-coated trays were employed to dry carrots and pears. Two trays with a selective absorber coating (1 mm thickness) were used, each loaded with 600 g of sliced carrots and pears. Similarly, two additional trays with a non-selective absorber coating were utilised. Furthermore, the performance of both selective and non-selective absorber-coated trays was compared with conventional open sun drying. The selective absorber-coated tray demonstrated higher thermal energy absorption and enabled the drying of carrots within 2 days, resulting in a weight loss of 529 g. In contrast, owing to the higher fructose content in pears, approximately 7 days were required to achieve a weight loss of 480 g. Overall, more effective drying was observed in the selective absorber-coated tray compared with the non-selective absorber-coated tray and open sun drying. A two-way ANOVA was performed to evaluate the effectiveness of the drying process across the examined methods. The higher F-statistic value of 63.566 indicates significant biological differences between carrots and pears, validating the comparison of their drying behavior. In addition, the F-statistic value of 5.099 for the drying method suggests a notable variation between selective and non-selective absorber-coated trays. It is concluded that the selective absorber-coated tray facilitates enhanced heat transfer to both carrots and pears. Following the ANOVA analysis, a Random Forest (RF) regression model was applied to the selective absorber-coated tray. Notably, the solar dome inner cover temperature exhibited a feature importance of 0.958, indicating its dominant role in increasing the tray temperature and improving drying performance. The average difference between actual and predicted tray temperatures was 0.16°C, demonstrating that the RF model is stable and reliable, with an R<sup>2</sup> value of 0.975. The integration of machine learning approaches in solar dryers can support predictive maintenance and reduce operational costs.},
DOI = {10.32604/ee.2026.080474}
}