With growing global awareness regarding the environmental consequences associated with non-renewable fossil fuels, the demand for clean and renewable energy sources has surged. Solar photovoltaic (PV) technology has emerged as a promising alternative, but accurate solar irradiance forecasting remains a challenge due to the variable nature of solar radiation. This thesis explores the potential of deep learning techniques, specifically employing a 2D-Convolutional Long-Short-Term Memory (ConvLSTM2D) architecture, to develop real-time and future solar irradiance forecasting models. The study focuses on Global Horizontal Irradiance (GHI) forecasting, utilizing multi-resolution satellite images from the Meteosat family of geostationary satellites managed by EUMETSAT as exogenous inputs.