Synergistic Computational and Machine Learning Models for Optimized Solar Thermal Energy Harvesting

Abstract

Solar thermal energy harvesting represents a critical frontier in renewable energy technologies, offering substantial potential for sustainable power generation across both direct and indirect energy conversion pathways. This research presents a comprehensive investigation of synergistic computational and machine learning models specifically designed to optimize solar thermal energy harvesting systems through systematic parameter optimization and intelligent control strategies. The study integrates advanced computational fluid dynamics methodologies with multi-parameter optimization algorithms and thermoelectric energy harvesting circuits to create hybrid frameworks that significantly enhance energy conversion efficiency. Through systematic analysis of thermal gradient modeling, heat transfer optimization, and predictive energy forecasting using Response Surface Methodology and machine learning algorithms, this research demonstrates that combined computational and optimization approaches can achieve substantial improvements in energy harvesting efficiency compared to conventional fixed-parameter methods. The proposed hybrid framework incorporates real-time environmental parameter monitoring and multi-objective optimization to dynamically adjust system parameters including collector orientation, thermal storage management, and thermoelectric generator configurations for optimal performance. Experimental validation conducted using thermoelectric generator systems with integrated power management circuits confirms the robustness and practical applicability of the developed optimization models. The findings indicate that systematic parameter optimization using computational methods can achieve power output improvements with correlation coefficients exceeding 0.94 across diverse environmental conditions. Furthermore, the integration of advanced power management circuits within the computational framework demonstrates enhanced energy harvesting capacity and extended operational periods. This research contributes significantly to the advancement of intelligent solar thermal systems by providing a comprehensive methodology for real-time optimization and systematic parameter control, ultimately facilitating broader adoption of renewable energy technologies in industrial and residential applications.