## 2 Literature Review
### Overview of PV Power Prediction
#### 2.1.2 Statistical and AI Methods
employed random forest and gradient-boosting regressor techniques to produce both deterministic and probabilistic estimates of solar energy output based on data collected over an eleven-month period. Some of their results showed that the RF model outperformed the other models they examined in terms of producing probabilistic estimations [37]. [PERSON] et al. [38] compared different ML models in PV panel power estimation. Support Vector Machine Regression (SVMR) provided the highest accuracy with R\({}^{2}\) = 0.99, MSE = 0.038, and MAE = 0.17. They stated that SVMR can better model environmental factors such as solar radiation, temperature, and humidity. [PERSON] et al. [39] analyzed a one-year dataset in hourly intervals collected from a 10 MW PV plant (Masdar) using regression tree ensemble, an SVM, GPR, and an ANN. Based on their results, they stated that GPR showed the best performance with the lowest prediction errors. [PERSON] et al. [40] propose short-term forecasting of PV power generation using an optimized Extreme Learning Machine (ELM) model to reduce the impact of solar energy variability on grid instability. The ELM model is optimized to forecast PV generation online in the short term. They stated that the proposed method provides stable energy management in the microgrid system despite the changes in irradiance, temperature, and Alternating Current (AC) load demand. [PERSON] et al. compared DT, RF, XBoost, and SVM regression models with 7-month meteorological data from NASA/POWER CERES/MERRA2 for estimating irradiance and temperature, two important parameters for energy production in PV systems, highlighting the importance of accurate weather forecasts for PV system operation and the difficulties associated with traditional meteorological models [41]. [PERSON] et al. employed SVM and Gaussian Process (GP) in their predictions for various time horizons, ranging from one day to fifteen days, in a study conducted in Riyadh, Saudi Arabia, in 2025. They stated that the SVM is an efficient algorithm for processing high-dimensional data [42]. In recent years, explainable AI (XAI) techniques have been developed to increase the interpretability of ML models and make the obtained outputs more understandable. The primary goal of XAI is to enable users of ML models to better understand their behavior without compromising the model's performance [43]. DL models: DL is an extension of ML created to address a challenging issue with large amounts of data. Since it has been noted that ML models in PV forecast the research plateau as the quantity of training data increases, DL is employed in forecasting studies using large datasets. The more input data, the better DL models perform [44]. Since PV power forecasting is mostly conducted with sequential data, the main DL methods recommended are Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Networks-LSTM (CNN-LSTM) [5]. RNN's recurrent architecture and memory units allow it to remember power changes over time. Consequently, RNN is a very short-term PV power forecasting model that takes intraday and interday PV power into account [45]. In the literature, some studies use only RNN in PV power forecasting [46; 47]. However, recently, RNN has been proposed more as a hybrid with other models. For example, [PERSON] et al. propose a hybrid prediction model that integrates traditional electrical components with RNN-LSTM in their 2024 study. The model has shown higher accuracy, adaptability, and robustness to data quality than existing methods, with 3.21% Mean Absolute Percentage Error (MAPE) and 0.0423 RMSE values [48]. LSTM is a unique type of RNN that can learn both short-term and long-term dependencies and is designed to avoid long-term dependencies [49]. [PERSON] et al. [50] applied the LSTM to predict the production of a solar power plant in Brazil. They studied the effect of the number of hidden layers on the prediction accuracy and found that the 4-layer LSTM model provided the lowest error rate.