Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/130960
Título: Sky Image Classification Based on Transfer Learning Approaches
Autores/as: Hernández López, Ruymán 
Travieso González, Carlos Manuel 
Ajali Hernández, Nabil Isaac 
Clasificación UNESCO: 3325 Tecnología de las telecomunicaciones
Palabras clave: Cloudiness classification
Deep learning
Transfer learning
Convolutional neural networks
EfficientNet models, et al.
Fecha de publicación: 2024
Publicación seriada: Sensors (Switzerland) 
Resumen: Cloudy conditions at a local scale pose a significant challenge for forecasting renewable energy generation through photovoltaic panels. Consequently, having real-time knowledge of sky conditions becomes highly valuable. This information could inform decision-making processes in system operations, such as determining whether conditions are favorable for activating a standalone system requiring a minimum level of radiation or whether sky conditions might lead to higher energy consumption than generation during adverse cloudy conditions. This research leveraged convolutional neural networks (CNNs) and transfer learning (TL) classification techniques, testing various architectures from the EfficientNet family and two ResNet models for classifying sky images. Cross-validation methods were applied across different experiments, where the most favorable outcome was achieved with the EfficientNetV2-B1 and EfficientNetV2-B2 models boasting a mean Accuracy of 98.09%. This study underscores the efficacy of the architectures employed for sky image classification, while also highlighting the models yielding the best results.
URI: http://hdl.handle.net/10553/130960
ISSN: 1424-8220
DOI: 10.3390/s24123726
Fuente: Sensors [1424-8220] , v. 24 (12), 3726, (Junio 2024)
Colección:Artículos
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