## 1 Introduction
At the same time, one of the most effective algorithms is the multi-criteria genetic optimization algorithm, proposed by a group of scientists led by [PERSON] in 2002 and implemented in the search for optimal solutions [28; 29; 30]. Accurate prediction of photovoltaic performance under general conditions is not possible using the specifications of photovoltaic module manufacturers. In general, literature studies investigate how the degradation mechanism, degradation modes, and degradation rates affect the efficiency of a photovoltaic module. Studies typically concentrate on issues such as cell cracks, coating oxidation, and glass fouling, as they directly impact the system efficiency. The literature has reported several studies that use system efficiency. These studies applied a unique Simulink system modeling method to analyze the electrical output characteristics of a dense array. The experimentally measured results were in reasonable agreement with the simulated ones but with a deviation of more than 3% for the maximum output power. The literature has recently reported the rise in popularity of machine learning algorithms and their successful prediction of photovoltaic power. For instance, some researchers used an artificial neural network (ANN) algorithm to research and predict the maximum output power. The related study underscored the ability to successfully predict output power data, even with a limited data set. Researchers investigated the predictability of output power using only a neural network with a radial basis function. In addition to the ANN, there are also several studies that used support vector machine (SVM) methodsto predict output power in photovoltaic systems. When an ANN and SVM are combined, we observe that the performance of these two machine learning algorithms closely matches each other. However, an SVM requires less input data for training and excels at solving nonlinear problems due to its automatic optimization step. In contrast, an ANN has a more complex structure than an SVM. [PERSON]'s study investigated solar power forecasting using the nearest neighbor (k-NN) algorithm. The results achieved with the k-NN algorithm were comparable to those of ANN, SVR (Support Vector Regression), exponential smoothing, and ARIMA. In the relevant study, the authors used only two metrics to evaluate the effectiveness of the algorithms: MAE and RMSE. The results indicated that k-NN had the most accurate forecasting results. Another study used ANN, SVM, and KF (Kalman Filter) techniques to predict power. Some researchers are improving hybrid models to improve the accuracy of power forecasting results. For instance, some researchers studied a combination of wavelet transform (WT), particle swarm optimization (PSO), and SVM using meteorological climate variables as input data. The results of the hybrid models were better in terms of accuracy than those using only the algorithm. Other researchers also studied modifying neural networks to enhance the accuracy of PV power results. The SVM performed better than the ANN at predicting solar power when it was trained with the original data on solar radiation, temperature, and relative humidity and using a fuzzy preprocessing technique. Furthermore, different machine learning algorithms (SVM, DL, and random forest (RF)) were compared. We discussed the projected solar power and the prediction success of these algorithms in terms of RMSE, MAE, and bias. All machine learning algorithms had satisfactory results. Another study optimized an ANN using a genetic algorithm (GA), k-NN, and ARMA to achieve more accurate power forecasting results. The study used the input parameters of cloud position, solar output power, solar radiation, and clear sky index. [PERSON] and [PERSON] used a GA to select the input parameters in their study. The authors used an ANN to predict the solar power. The study uses a robust model and ARIMA, k-NN, ANN, and ANN-GA techniques to predict solar power output.