## 2 Literature Review
### Overview of PV Power Prediction
#### 2.1.2 Statistical and AI Methods  
Statistical methods stand out in solar energy estimation because they are fast, are efficient, and have low computational costs for short and medium-term estimations [21; 22]. Future PV power output is estimated using a statistical method, which utilizes historical time series data measurements as input data [23; 24]. There are many benefits to using statistical approaches. Even if there are gaps in the data, they can still function after being trained using examples from the data. They can generate and generalize predictions after being trained [25]. Time series analysis, ML, and DL are the categories into which statistical techniques can be separated [22]. The statistical methods are explained as follows:  
Time series analysis methods: These methods include statistical techniques for predicting future energy production using historical production data. These methods analyze historical data from PV systems to reveal specific patterns and trends and create mathematical models to predict future production. Since PV power is variable and influenced by weather conditions, solar irradiation, and system configurations, time series analysis techniques are crucial for modeling and predicting PV power. These approaches, ranging from complex ML models to traditional statistical techniques, allow for efficient PV power estimation [3; 26; 27]. The primary time series analysis methods employed for PV power estimation are Autoregressive (AR) and Moving Average (MA) models as well as ARMA, which combines AR and MA models, and ARIMA, which incorporates the difference process (integrated) to render the time series stationary. [PERSON] et al. [3] performed PV power forecasting using time series analysis in large-scale PV plants, which showed that ARIMA is effective in short-term forecasting. [PERSON] and [PERSON] [28] compared the ARIMA model with other ML techniques in solar energy forecasting and stated that ARIMA is a competitive method in certain scenarios. Time series data with periodic characteristics can be predicted using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, which is based on the ARIMA model and considers periodic components [29]. As a generalization of the ARIMA model, the Autoregressive Moving Average with Exogenous Variables (ARMAX) offers greater practical flexibility. Unlike ARIMA, the ARMAX model allows for the inclusion of external data; however, its application in PV power prediction has been relatively limited so far [30]. ML models: The foundation of ML models is a more intricate collection of algorithms that enable the model to identify patterns and connections in the data independently. ML outperforms conventional techniques in many situations due to its ability to manage non-linearities, integrate multiple data sources, and adapt to changing circumstances. To fully realize their potential, however, issues such as weather fluctuations and data quality must be addressed [31; 32]. The leading ML models for PV power prediction are Artificial Neural Networks (ANNs), random forests (RFs), Support Vector Machines (SVMs), Gaussian Process Regression (GPR), and Extreme Learning Machines (ELMs) [31; 33; 34]. [PERSON] et al. [35] proposed an innovative selective ensemble method to increase the PV power prediction accuracy and reduce the computational burden using ANNs. Multipleindependently trained ANN models were created, and the prediction performance of each model was evaluated. An ensemble was formed by selecting models performing above a certain threshold value. The proposed selective ensemble method significantly increased the accuracy of PV power prediction. In 2024, [PERSON] et al. [36] identified RF as the best-performing model for predicting power output from PV plants through rigorous data preprocessing, feature selection, and model evaluation in their forecasting study on a PV plant in Algeria. [PERSON] et al.