## 1 Introduction  
The increasing demand for sustainable energy sources has led to a significant growth in the use of solar energy. In particular, the Spanish government, as set out in the National Integrated Energy and Climate Plan 2021-2030 (PNIEC), anticipates electricity generation with a total installed capacity of 157 GW by the year 2030, of which 37 GW is expected to come from solar PV [1]. For this reason, it is considered important to develop reliable predictive models for solar radiation [2]. One of the most important factors influencing the quality of solar forecasts is solar variability [3]. This variability is mainly a spatio-temporal phenomenon and, in recent years, researchers in this field have introduced the use of machine learning methods due to their capacity for processing large amounts of data. Machine learning methods have the capability of modelling this phenomenon more accurately [4], and have been extended to solar forecasting, showing significant improvements over traditional statistical methods [5]. In the deep learning field of machine learning, convolutional neural networks (CNNs) have exhibited remarkable capabilities in learning intricate patterns from spatially and temporally complex datasets, such as satellite and sky images [6; 7] or graphs [8]. Among the various architectural modifications introduced to enhance the performance of CNNs [9; 10; 11; 12; 13], dilated convolutions [14] have emerged as a novel architectural refinement. They have been used with univariate time series [15] for video-based crowd estimation [16], for speech emotion recognition [17], and for dense prediction [18], among other tasks. Dilated convolutions enable networks to achieve wide receptive fields with minimal layers, maintaining input fields throughout the network while also ensuring computational efficiency [19]. [PERSON] et al. [15] used stacked dilated convolutional layers for load forecast with univariate time series and showed that the model surpassed other state-of-the-art models by capturing the local trends and seasonality. [PERSON] et al. [20] used dilated convolutional layers to extract hidden time characteristics as part of a more complex multivariate time-series forecast model. The model was applied to traffic and engine datasets and obtained better performances than other baselines. In the same way, [PERSON] et al. [21] used a stack of dilated convolutional layers with different values of dilation to capture temporal dependencies and to make predictions for the base time series. The convolutional layers were part of a more complex model that also presented an attention scheme. The model was used for the ultra-short-term spatio-temporal forecasting of renewable resources (wind speed and solar generation). [PERSON] et al. [22] used a genetic-based attention network for photovoltaic power forecasting and utilized dilated convolution as the non-linear component to simplify the network structure and learn the spatial and temporal dependencies between historical weather data. The authors employed two dilated convolutional layers that concatenated with a Hadamard product used as an input for a more complex model. In [23], the authors review the most widely used deep learning methods for solar radiation prediction. Recurrent networks are highlighted and, in particular, long short-term memory (LSTM), gated recurrent unit (GRU), and CNN networks combined with other networks or methods are mentioned. On the other hand, in [24], the authors concentrate the study on vision methods of deep learning for solar forecasting. CNNs stand out with their hybridization with other networks to improve results. All of these models offer major advantages due to their ability to work with sequential data, such as time series. They are able to learn complex patterns from the data more easily. They can handle large volumes of data. They are able to process data from different sources.