Journal of Thermal Engineering and Applications Review Article
AI-Based Predictive Modeling of Thermal Efficiency and Heat Loss in Solar Thermal Collectors
Abstract
Solar thermal collectors are widely used for water heating, agricultural drying, industrial process heating and other low- and medium-temperature thermal applications. Their thermal performance is strongly influenced by solar irradiance, fluid temperature, ambient temperature, mass flow rate, wind speed and operating conditions. Conventional analytical models can describe collector behavior using energy- balance equations, but their predictive performance may decrease under highly variable environmental and operating conditions. This study proposes an artificial-intelligence-based framework for predicting the thermal efficiency and heat loss of a flat-plate solar thermal collector. Solar irradiance, ambient temperature, inlet fluid temperature, outlet fluid temperature, mass flow rate, absorber temperature and wind speed are considered as model inputs. Random Forest (RF), Support Vector Regression (SVR), Artificial Neural Network (ANN) and Extreme Gradient Boosting (XGBoost) models are developed and comparatively evaluated using coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). A physics-based thermal model is first used to establish the relationship between useful heat gain, incident solar energy and heat loss. A simulated dataset containing 720 operating observations is subsequently used to demonstrate the proposed machine-learning workflow. The illustrative results indicate that XGBoost provides the highest predictive accuracy for thermal efficiency, with an R² of 0.94, while ANN provides comparable performance. For heat-loss prediction, XGBoost achieves an R² of 0.93. Analysis indicates that solar irradiance, absorber temperature, fluid inlet temperature and mass flow rate are among the dominant variables affecting collector performance. The proposed framework demonstrates the potential of combining thermal-energy equations and machine learning for rapid prediction and operational assessment of solar thermal collectors.
Keywords
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