Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11765/16263
Retrieval of Solar Shortwave Irradiance from All-Sky Camera Images
Title: Retrieval of Solar Shortwave Irradiance from All-Sky Camera Images
Authors: González-Fernández, DanielRomán, RobertoMateos, DavidHerrero del Barrio, CeliaCachorro, Victoria E.Copes, GustavoSánchez, RicardoGarcía Cabrera, Rosa Delia ORCID RESEARCHERID Doppler, LionelHerrero-Anta, SaraAntuña-Sánchez, Juan CarlosBarreto Velasco, África ORCID RESEARCHERID SCOPUSID Autor AEMETGonzález, RamiroGatón, JavierCalle, AbelToledano, CarlosFrutos Baraja, Ángel Máximo de
Keywords: All-sky cameras; Sky images; Convolutional neural network; Cloud modification factor; Shortwave global horizontal irradiance; Antarctic
Issue Date: 2024
Publisher: MDPI
Citation: Remote Sensing. 2024, 16(20), 3821
Publisher version: https://doi.org/10.3390/rs16203821
Abstract: The present work proposes a new model based on a convolutional neural network (CNN) to retrieve solar shortwave (SW) irradiance via the estimation of the cloud modification factor (CMF) from daytime sky images captured by all-sky cameras; this model is named CNN-CMF. To this end, a total of 237,669 sky images paired with SW irradiance measurements obtained by using pyranometers were selected at the following three sites: Valladolid and Izaña, Spain, and Lindenberg, Germany. This dataset was randomly split into training and testing sets, with the latter excluded from the training model in order to validate it using the same locations.
Sponsorship : The research has been supported by the Ministerio de Ciencia e Innovación, with the grant no. PID2021-127588OB-I00, and the Junta of Castilla y León with the grant no. VA227P20. This work is part of the project TED2021-131211B-I00 funded by MCIN/AEI/10.13039/501100011033 and European Union, “NextGenerationEU”/PRTR. This research is based on work from COST Action CA21119 HARMONIA, supported by COST (European Cooperation in Science and Technology).
URI: http://hdl.handle.net/20.500.11765/16263
ISSN: 2072-4292
Appears in Collections:Artículos científicos 2023-2026


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