###### Abstract
Similarly, ref. [17] developed a hybrid ANN-Fuzzy MPPT method that combined the adaptability of ANN with the robustness of fuzzy logic, resulting in improved performance under dynamic conditions. Advantages of ANN-based MPPT methods include their ability to handle nonlinearities, adapt to changing conditions, and improve the overall efficiency of PV systems. However, their implementation requires sufficient training data, computational resources, and careful tuning of the network architecture [18]. RL is a machine learning paradigm that enables an agent to learn optimal actions through interaction with its environment. In the context of MPPT, the RL agent learns to adjust the operating point of the PV system to maximize energy output. RL-based MPPT methods are particularly effective in handling dynamic and uncertain environments, as they do not rely on predefined models or assumptions [19]. Recent studies have highlighted the potential of RL-based MPPT methods. For instance, ref. [20] proposed a Q-learning-based MPPT algorithm that outperformed traditional methods in terms of tracking accuracy and energy efficiency. Another study [21] employed a deep reinforcement learning approach, which utilized a deep neural network to approximate the Q-function, enabling the agent to handle high-dimensional state spaces and complex PV system dynamics. Key advantages of RL-based MPPT methods include their adaptability, model-free nature, and ability to optimize long-term performance. However, their implementation can be computationallyintensive and may require careful design of the reward function and exploration strategy [22]. The performance of MPPT methods can be evaluated based on different criteria such as tracking accuracy, convergence speed, computational complexity, and adaptability to changing conditions, as shown in Table 1.  
The integration of advanced MPPT methods, such as ANN and RL, has significantly improved the performance of PV systems by addressing the limitations of traditional approaches [12]. ANN-based methods excel in handling nonlinearities and adapting to changing conditions, while RL-based methods offer unparalleled adaptability and long-term optimization [23; 24]. However, the implementation of these methods requires careful consideration of computational resources, training data, and system design. In real environments, data quality can decrease significantly due to factors such as variable weather conditions, sensor failures, and photovoltaic panel aging. This can negatively affect both the performance of MPPT algorithms and the accuracy of AI-based models [25]. Weather conditions such as partial shading, rain, snow, and dust cause sudden changes in solar radiation and temperature data. These changes can cause noise and deviations in sensor measurements [26]. Sensor failures can produce incorrect or incomplete data, leading to the model making incorrect decisions. Panel aging leads to a decrease in efficiency over time, causing models trained on past data to no longer accurately reflect the current conditions [27]. Such data quality problems reduce the generalization ability and reliability of the model, especially in data-driven methods such as ANN and RL. In addition, the system's energy production estimate and MPPT performance may decrease. To solve these problems, regular maintenance and calibration of the sensors in the system should be performed, and anomaly detection and data cleaning algorithms should be applied. Models should be retrained periodically with current and diverse data, and physical changes such as panel aging should be taken into account in the training of the model. These measures will increase the reliability and efficiency of the system by reducing the negative effects of data quality degradation in a real-world environment [28; 29]. For traditional methods, finding the MPPT of photovoltaic systems under partial shading conditions is quite challenging because multiple local maxima occur. There are many studies in the literature which consider partial shading conditions.