Prepared by: Engineer Nourhan Thamer Assi With the rapid expansion of solar energy systems as a clean and sustainable source of power, improving energy conversion efficiency has become one of the most important technical challenges facing researchers and engineers. The efficiency of a solar system largely depends on its ability to extract the maximum possible power from photovoltaic (PV) panels under varying environmental conditions such as solar irradiance and temperature. Since the Maximum Power Point (MPP) continuously changes according to these conditions, Maximum Power Point Tracking (MPPT) techniques have been developed to ensure that the system operates at its highest possible power output at all times. Traditionally, algorithms such as Perturb and Observe (P&O) and Incremental Conductance have been widely used. Although these methods are relatively simple and practical to implement, they suffer from certain drawbacks, including oscillations around the maximum power point and reduced performance under rapidly changing weather conditions. With the significant advancement in artificial intelligence technologies, Artificial Neural Networks (ANN) have emerged as a more advanced and efficient solution to address these limitations. The use of neural networks in MPPT applications is based on their ability to learn the nonlinear relationship between system inputs—such as solar irradiance, temperature, voltage, and current—and the optimal operating point that yields maximum power. The neural network is trained beforehand using real or simulated data representing various operating conditions. After training, the network can directly predict the optimal voltage or current value without the need for continuous searching processes. As a result, oscillations are minimized, response speed is enhanced, and the overall system efficiency is improved. One of the main advantages of this technique is its high tracking accuracy, fast response to sudden environmental changes, and strong capability to handle the nonlinear characteristics of photovoltaic systems, making it suitable for advanced and high-power applications. However, its implementation requires sufficient and accurate training data, as well as careful network design, including the selection of layers and neurons to achieve a balance between performance and computational complexity. In conclusion, the application of neural networks in Maximum Power Point Tracking represents a significant advancement in the development of solar energy systems. It contributes to higher efficiency, reduced power losses, and improved operational reliability. With continuous progress in artificial intelligence technologies and decreasing costs of microprocessors, these intelligent solutions are expected to become a fundamental component in the design of future renewable energy systems.