## 3 Review and Evaluation of Solar Irradiance Forecasting Methods
### Statistical Methods
#### 3.1.2 AI Artificial Intelligence Techniques  
AI techniques have generated a great deal of interest in solar irradiance forecasting research due to their breakthroughs in optimization, pattern recognition, and classification. The most used AI techniques in irradiance forecasting are as follows:  
Artificial neural networks (ANNs). These are the most widely used machine learning techniques in solar power forecasting. The most common are MLPs, multi-layer perceptron structures, but due to the large number of topologies, it seems appropriate to propose only publications that review the solar forecasting techniques by the ANN, for example, [118; 119; 120]. k-Nearest neighbors (k-NN). It is one of the simplest machine learning methods; thus, for renewable energy forecasts, the prediction about future outcomes is based on historical data with a similar time of day and weather conditions. Deep learning (DL). Compared to traditional machine learning approaches, DL can perform feature extraction by itself and the improvement of learning performance is proportional to the dataset increase; therefore, it properly solves situations involving a large amount of data and hence, it is an effective technique in weather prediction. Various DL techniques can be highlighted in the field of solar forecasting, such as the restricted Boltzmann machine (RBM), deep belief network (DBNs), the automatic encoder AE, its stacked version (SAE), LSTM networks, stacked LSTM networks and convolutional neural networks (CNNs) and deep convolutional neural networks (DCNNs). For readers interested in this field, there are studies available that compare deep learning (DL) techniques for solar irradiance forecasting. Some examples of such investigations are in [121; 122; 123]. Mycielski graph-based models are used for prediction. In [124] a Mycielski-based model is proposed which considers the hourly recorded solar radiation data as a matrix and, starting from the last recorded value, tries to find the most similar submatrix pattern in history. Support vector machines (SVMs). Compared to the k-NN, SVMs operates effectively with small sample sizes while the k-NN is more sensitive to the size increase in the training dataset [125]. Random forest (RF). This has been widely used in many prediction fields due to its high tolerance to low information and improved fitting ability [126]. Markov chains. Normally utilized in hybrid systems, they has been widely used to generate sequences of the solar irradiance data, as well as to predict the daily global solar irradiance data by combining typically with neural networks [127]. Coupled autoregressive and dynamical system (CARDS). This model has been used in the literature for probabilistic predictions based exclusively on endogenous data. This model is characterized by decomposing GHI as the sum of a cyclic term and an autoregressive term [128; 129; 130; 131]. Numerous studies have investigated the effectiveness of combining or comparing different techniques in a hybrid system. Research studies such as [131; 132] have compared machine learning models with AR models, demonstrating the potential benefits of hybrid systems in forecasting and prediction tasks. In addition, Ref. [133] developed a forecasting model for solar power generation by combining ARMA with ARIMA. These studies illustrate the diverse range of techniques utilized to forecast solar radiation and highlight the potential benefits of utilizing a combination of techniques for enhanced accuracy and reliability. Different approaches have been proposed for solar irradiance forecasting, including data-driven methods and Markov switching models. For instance, Ref. [134] applies data-driven approaches such as the boosted regression tree (BRT), ANNs, and SVM boosted regression trees, as well as benchmarks with the AR and ARX models. Meanwhile, Ref. [135] presents the comparison of Adaptive Neuro Fuzzy Inference System (ANFIS) and Autoregressive Moving Average with eXogenous term (ARMAX) in forecasting global solar radiation. Wavelet neural networks (WNNs).