Optimizing Air Photovoltaic/Thermal Solar Collectors: Integrating a Fuzzy Logic Approach for Enhanced Efficiency

The increasing global energy demand necessitates improvements in renewable energy efficiency, particularly in solar energy systems. This study aims to develop a compact Photovoltaic/Thermal (PV/T) solar collector using air as the working fluid, designed to achieve higher efficiency and better space utilization compared to traditional systems. The performance of PV/T collectors is influenced by environmental factors such as solar intensity and ambient temperature, posing challenges in determining the optimal air mass flow rate. To address this, a fuzzy logic-based automated control system is developed, integrating a Weighted Subsethood-based algorithm and Fuzzy Subjective Evaluation, to dynamically optimize fan speed under varying conditions. This study highlights the importance of adaptive control in renewable energy systems and offers a scalable, transportable solution for maximizing solar energy utilization. This approach maintains the balance between input and output energy, demonstrating that the highest air mass flow rate is not always necessary for optimal performance.

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