The use of photovoltaic (PV) systems has been rapidly increasing over the years due to policies aimed at reducing CO2 emissions. However, the discontinuous nature of solar power represents a challenge for the efficient integration of PV systems into the electricity grid. Therefore, the use of solar radiation prediction models proves to be highly valuable for the smooth integration and reliable operation of the electricity grid. In the literature, several studies have been conducted using Numerical Weather Prediction (NWP) to forecast future solar radiation. However, in recent years, deep learning-based models have become dominant. Most of the models in the literature use convolutional or recurrent neural networks, such as Long Short-Term Memory (LSTM), which have shown the best performance.