TY - JOUR A1 - Huertas Tato, Javier AU - Galván León, Inés María AU - Aler Mur, Ricardo AU - Rodríguez Benítez, Francisco Javier AU - Pozo Vázquez, David T1 - Using a Multi-view Convolutional Neural Network to monitor solar irradiance Y1 - 2022 SN - 0941-0643 UR - http://hdl.handle.net/11268/10176 AB - In the last years, there is an increasing interest for enhanced method for assessing and monitoring the level of the global horizontal irradiance (GHI) in photovoltaic (PV) systems, fostered by the massive deployment of this energy. Thermopile or photodiode pyranometers provide point measurements, which may not be adequate in cases when areal information is important (as for PV network or large PV plants monitoring). The use of All Sky Imagers paired convolutional neural networks, a powerful technique for estimation, has been proposed as a plausible alternative. In this work, a convolutional neural network architecture is presented to estimate solar irradiance from sets of ground-level Total Sky Images. This neural network is capable of combining images from three cameras. Results show that this approach is more accurate than using only images from a single camera. It has also been shown to improve the performance of two other approaches: a cloud fraction model and a feature extraction model. KW - Análisis de datos KW - Inteligencia artificial KW - Ciencias del espacio LA - eng ER -