Enhancing Solar Energy Efficiency Integrating Neural Networks with Maximum Power Point Tracking Systems

This paper investigates the integration of artificial neural networks (ANN) into Maximum Power Point Tracking (MPPT) systems to enhance the efficiency and performance of solar photovoltaic (PV) systems. Conventional MPPT controllers, such as Perturb and Observe (P&O) and Incremental Conductance, often face challenges in maintaining optimal efficiency under rapidly changing environmental conditions. To address these challenges, this study proposes an innovative approach leveraging the adaptive learning capabilities of ANN. Extensive datasets, including solar irradiance, temperature, and power output under various conditions, were collected to design and train the ANN model. The ANN architecture features a multilayer structure with advanced activation functions, enabling effective handling of the nonlinear behavior of solar PV systems. The results demonstrated that ANN-based MPPT systems improved efficiency by up to 15% compared to traditional techniques, achieved a 25% increase in response speed, and exhibited greater stability, particularly under partial shading and rapid irradiance variations. This research not only enhances the efficiency of solar energy systems but also highlights the significant potential of machine learning in renewable energy technologies. Future studies could explore scaling this approach to accommodate diverse environmental conditions, paving the way for intelligent, AI-driven renewable energy solutions.