###### Abstract
Despite their utility, these models lack robustness and can produce null or incomplete values under certain conditions [16]. Machine learning (ML) approaches have become popular in the last decade, as these models can automatically learn and improve forecasting by comparing real and predicted data. Common methods include support vector machines (SVMs) [17], multi-layer perceptron (MLP) [18], extreme learning machines (ELMs) [19], and artificial neural networks (ANNs) [20]. However, the inherent volatility and randomness of irradiance makes more complex models have problems in learning from these data. Therefore, to solve this problem, hybrid models combine different architectures, such as SVMs, MLP, ELMs, and ANNs, to improve prediction efficiency and accuracy in PV power estimation. In addition, variable decomposition techniques, such as empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and wavelet decomposition (WT), are used to capture temporal and long-term features. [PERSON] et al. [21] improved a hybrid model by combining EMD and EEMD, identifying clear sky patterns using autoregression (AR) and ANN models. [PERSON] et al. [22] proposed a hybrid SOM-BP model that applies EEMD to decompose data into subsequences, enhancing network performance. [PERSON] et al. [23] utilized wavelet analysis, together with SVMs and ANNs, to improve irradiance predictions. Deep learning (DL) models are currently the state of the art for renewable energy forecasting. DL models try to efficiently extract temporal and spatial features, which makes them particularly suitable in renewable energy applications, although they rely on large datasets. For example, [PERSON] et al. [24] proposed a hybrid model that integrates a convolutional neural network (CNN) and a long short-term memory network (LSTM) for PV forecasting, achieving high accuracy. [PERSON] et al. [25] developed a combination of a CNN and bidirectional gated recurrent unit network (GRU), achieving an optimal combination for energy forecasting. [PERSON] et al. [26] built a model using CNN and a multi-layer bidirectional LSTM (M-BLSTM), outperforming simple LSTM and CNN models. Additionally, [PERSON] et al. [27] introduced a hybrid CNN-LSTM model for solar irradiance forecasting, which outperformed conventional machine learning and deep learning architectures. In [28], the authors presented two hybrid models, a Conv-LSTM and a hybrid CNN-LSTM, for solar energy prediction in a PV plant, demonstrating superior performance compared to base LSTM models. In [29], the PV-Net DL architecture was presented to predict PV power at 24 h. This approach uses Conv-GRU, a hybrid version of GRU that includes convolutional layers, instead of the standard GRU. This modification allows for a more accurate extraction of PV energy characteristics. [PERSON] et al. [30] developed a hybrid approach that integrates principal component analysis (PCA) with an ANN and an analogous ensemble (AnEn) to predict wind power and solar irradiance. This method uses PCA to decrease the dimensionality of the numerical weather forecasting (NWP) data, while the ANN and AnEn are used to train the model and generate the final forecasts. [PERSON] et al. [31] proposed a hybrid multivariate WPD-CNN-LSTM-MLP model based on a hybrid multi-frame structure with multivariate inputs combining wavelet packet decomposition (WPD), a CNN, LSTM, and MLP. These inputs include hourly solar irradiance and three climate variables, namely temperature, relative humidity, and wind speed, and their combination.