Por favor, use este identificador para citar o enlazar este ítem: https://hdl.handle.net/20.500.12008/32272

Título:

DeepCloud : Intra-day satellite prediction of cloudiness using deep learning strategies

Otros títulos:

Coordinador:

Director:

Compilador:

Autor:

Camiruaga, Ignacio
Herrera, Andrés
Mozo, Franco

Tutor:

Alonso Suárez, Rodrigo
Castro, Alberto
Marchesoni, Franco

Tipo de documento:

Tesis de grado

Editor:

Palabras clave:

Solar forecast
U-Net
Deep learning
Satellite images
GOES-16 satellite
Pronóstico solar
Aprendizaje profundo
Imágenes satelitales
Satélite GOES-16

Descriptores:

Año de publicación:

2022

Contenido:

Resumen:

This project analyzes deep learning techniques applied to satellite-based cloudiness prediction, a vital component of a solar forecasting solution. The techniques can learn from a dataset to make extrapolation into the future of a sequence of images, a process usually named satellite nowcasting. In this way, intra-day image forecasting is addressed up to 5 hours into the future, with a 10-minute periodicity. The images used are from the GOES-16 geostationary satellite, covering a large portion of southeast South America, including Uruguay, the main region of interest. The new deep learning techniques are compared against strong baselines in the field, such as the persistence and fine-tuned Cloud Motion Vectors strategies, which were previously analyzed for this region in recent studies. Several state-of-the-art architectures are implemented and evaluated over different well-known computer vision metrics as well as forecasting metrics. Our results showed the ability of deep learning models to account for complex atmospheric dynamics and make accurate predictions in a short time span. The main contribution is a deep-learning model based on the U-Net architecture that surpasses in performance all the other state-of-the-art models implemented on this dataset. The new model is presented along with detailed ablation studies and thorough evaluations, that shed light on the behavior and many potential variations of the deep learning solutions.

Descripción:

Títulos obtenidos: Ignacio Camiruaga, Ingeniero en Computación; Andrés Herrera, Ingeniero electricista; Franco Mozo, Ingeniero electricista.

Editorial:

Udelar.FI.

EN:

Financiadores:

Citación:

Camiruaga, I., Herrera, A. y Mozo, F. DeepCloud : Intra-day satellite prediction of cloudiness using deep learning strategies [en línea]. Tesis de grado. Montevideo : Udelar. FI. IIE : INCO, 2022.

Citación:

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Documento del Gobiberno:

URI:

https://hdl.handle.net/20.500.12008/32272

ISBN:

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ISSN:

ISMN:

Otros identificadores:

Título Obtenido:

Ingeniero Electricista
Ingeniero en Computación

Facultad o Servicio que otorga el Título:

Universidad de la República (Uruguay). Facultad de Ingeniería.

Licencia:

Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)

Cobertura geográfica:

América del Sur
Uruguay

Cobertura temporal:

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Tesis de grado
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CHM22.pdf Tesis de grado 29.62 MB Adobe PDF
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