Year: 2026
Type: conference-paper
Abstract: In this study, an artificial intelligence (AI)-assisted model predictive control (MPC) method is developed for enhancing the thermal efficiency of solar water heating systems (SWHS). The efficiency of solar water heating systems depends on solar irradiance, water temperature, and environmental factors, which can be addressed by developing a machine learning (ML)-based predictive MPC method. A predictive method is developed for predicting outlet water temperature based on solar irradiance and water temperature, while an MPC method is developed for improving efficiency. The simulation results have demonstrated that an AI-based MPC method can predict outlet water temperature with high accuracy for a given prediction horizon during changes in solar irradiance, with high agreement between expected and predicted values. The predictive method has an R² value of 0.9844, indicating high accuracy in predicting outlet water temperature. Thus, this study showed that artificial intelligence model predictive control can improve the efficiency of solar water heating systems. Moreover, the results indicate that the solar water heating system can still accurately predict outlet water temperature despite changes in solar irradiance. Therefore, the efficiency of solar water heating systems can be improved by implementing an artificial intelligence model predictive control.
Institution: University of Botswana
Field: Energy
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7163065129