## 2 Literature Review
### Overview of PV Power Prediction
#### 2.1.3 Hybrid Methods  
To cope with the inherent complexity of solar energy and the unpredictability of meteorological conditions, hybrid methods that combine the capabilities of different approaches are becoming increasingly popular for PV power forecasting. With hybrid methods, projections become more reliable and accurate, facilitating better operational planning and integration of PV power into power grids [59; 60]. Preprocessing data, building forecast models, and fine-tuning model parameters are just a few of the stages involved in solving the forecasting problem addressed by hybrid approaches [61]. The hybrid approach combines physical and statistical methods, utilizing optimization algorithms to leverage the strengths of each method while mitigating their limitations [62]. [PERSON] et al. employed a hybrid RF and XGBoost model, combined with a single ANN and RF, in their forecasting studies using operational data from a grid-connected solar power plant with a capacity of 180 kWp. They stated that ANNs provided the best accuracy for one-day-ahead forecasts, RFs for two-week-ahead and one-month-ahead forecasts, and the hybrid model consisting of XGBoost and RF for one-week-ahead forecasts [63]. [PERSON] et al. aimed to combine the nonlinear data processing capability of ANNs with the capacity of LSTM to capture long-term dependencies in time series data, thereby increasing the accuracy of PV power estimates. As a result of the study, they stated that the model showed superior performance by achieving lower error rates (MAE, RMSE, etc.) in terms of error metrics and provided higher estimation accuracy compared to ANN or LSTM models [64]. [PERSON] and [PERSON] performed feature selection and optimization processes using the RF method for short-term PV power prediction, thereby increasing prediction accuracy by eliminating unnecessary features. They employed the Symplectic Geometry Model Decomposition (SGMD) technique to enhance input features and opted for the Bi-directional Long Short-Term Memory (BiLSTM) model in the prediction process. They also increased the prediction's stability by optimizing the BiLSTM model's hyperparameter settings with the Gray Wolf Optimization Algorithm (GWO). The study showed that the developed hybrid model is scalable and robust in different climate conditions and PV technologies and verified its applicability to real-world scenarios [65]. [PERSON] et al. PV power forecasting is performed with a hybrid model based on Variational Mode Decomposition (VMD)-Whale Optimization Algorithm (WOA)-LSTM. The model tested for a 1.8 MW system in Yulara, Australia, achieved high accuracy with MAE: 15.247 kW, RMSE: 19.753 kW, MAPE: 4.405%, and R\({}^{2}\): 0.997. The results show that the model successfully predicts PV generation fluctuations [61]. [PERSON] et al. investigated five different ML models (Multiple Linear Regression (MLR), Decision Tree Regression (DTR), random forest regression (RFR), Support Vector Regression (SVR), Multi-Layer Perceptron (MLP)) to improve solar energy production forecasting. They optimized their hyperparameters with the Chimpanzee Optimization Algorithm (ChOA). Among the models tested with data from a 264 kWp PV system in Jordan, the MLP model showed the best performance (RMSE: 0.503, MAE: 0.397, R\({}^{2}\): 0.99). They emphasized that the results proved that ChOA effectively improves forecasting accuracy [66]. [PERSON] et al. proposed the Enhanced and Improved Beluga Whale Optimization (EIBWO) to optimize the internal parameters of ELM. The R\({}^{2}\) of their proposed EIBWO-ELM exceeded 0.99, highlighting its efficient ability to adapt to PV power generation [67]. [PERSON] et al. utilized real data obtained from the experimental PV generation system installed at the Faculty of Engineering, Misan University, Iraq, to propose a short-term power forecast model.