The growing global demand for clean, sustainable energy has driven extensive research into renewable energy technologies, with solar energy emerging as a highly promising solution. Solar photovoltaic (PV) systems are increasingly adopted for their ability to convert sunlight into electricity, providing an environmentally friendly alternative to fossil fuels. However, the performance of PV systems is significantly influenced by environmental factors, particularly solar irradiance and temperature, which lead to fluctuations in power output. This study explores the application of Artificial Intelligence (AI)-based Maximum Power Point Tracking (MPPT) techniques to optimize the efficiency of PV systems. AI-driven MPPT controllers, incorporating machine learning, fuzzy logic, and genetic algorithms, offer enhanced adaptability, responsiveness, and efficiency compared to traditional methods. The research focuses on the design, development, and evaluation of an AI-optimized MPPT controller prototype, demonstrating the potential of AI to overcome the limitations of conventional MPPT techniques. This optimization enhances the efficiency, stability, and scalability of solar energy systems, pa
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