## 3 Review and Evaluation of Solar Irradiance Forecasting Methods
### Statistical Methods
#### 3.1.2 AI Artificial Intelligence Techniques
WNNs have improved generalization properties compared to ANNs, making them more suitable for modeling high-frequency signals such as local transients and intermittency [136; 137; 138]. Ref. [139] presents a solar irradiance forecasting method for remote microgrids based on the Markov switching model to schedule the energy resources in these microgrids. Moreover, Ref. [140] proposes a robust deep learning approach based on an automatic encoder-triggered recurrent unit (AE-GRU) for short-term solar energy forecasting. These various techniques can potentially enhance the accuracy and reliability of solar irradiance forecasting, highlighting the need to explore and compare different methods to improve the performance of such systems. In contrast, several research studies have employed machine learning or deep learning techniques, either independently or in combination with some of the techniques previously discussed. This is due to the vast amount of data that needs to be processed. These approaches demonstrate the versatility and adaptability of these techniques in managing large and complex datasets for accurate solar radiation forecasting. A prime example is [141], which suggests a convolutional long short-term memory (CLSTM) system that combines a deep learning convolutional neural network CNN with an LSTM network. Following this line, in [142], a spatio-temporally bound deep network based on CNNs and LSTM is proposed and in [143], a spatio-temporal solar radiation prediction model based on a convolutional graph network and LSTM is proposed. For instance, Ref. [144] designs a model in which the deep neural network (DNN) uses the reconstructed values estimated by four LSTM models with temperature data and statistical characteristics to predict PV power values. Ref. [145] uses a CNN-assisted deep echo state network that uses multiple time-scale dynamic learning repositories to generate short-term solar power forecasts. However, Ref. [146] associates the deep learning algorithms such as DBN and AE with LSTM to compare forecasts of a standard MLP in 21 solar power plants. Ref. [147] evaluates four scenarios (long window, short window, a Principal Component Analysis (PCA), and a clear sky model) with three typical forecast horizons by means of an irradiance database combined with the information measurements and an extreme learning machine (ELM). However, Ref. [148] follows feature extraction with a convolutional autoencoder and a K-means clustering algorithm compares and analyses the results with CNN, LSTM, and ANN models. Ref. [42], instead, compares MLP, CNN, and LSTM models using historical photovoltaic power values and sky images as the input. Ref. [149] proposes a deep CNN with a hybrid data input composed of sky images and PV panel output data. In addition, there are studies that propose innovative hybrid approaches to predict global solar radiation (GSR). For instance, Ref. [150] introduces a new 4-phase hybrid CXGBFR framework that combines a deep learning convolutional neural network, extreme gradient boosting with random forest regression, and a Harris Hawks optimization for initial function selection. Similarly, Ref. [151] compares several AI-based alternatives, including MLP, Random Forest Regressor, and Gradient Boosted Trees, to predict cloud shadow locations over solar panels. These studies showcase the continued efforts to develop and improve hybrid approaches that leverage the strengths of different techniques to enhance solar radiation forecasting. In general terms, the common methodology of research utilizing machine learning and deep learning coincides with that illustrated in Figure 6.  
Figure 6: ML-DL methodology. This figure outlines the machine learning and deep learning methodology employed in the study. To conclude, publications comparing and evaluating AI techniques for solar forecasting are also provided [152; 153; 154; 155].