Título:
An analysis of satellite-based Machine Learning models to estimate global solar irradiance at a horizontal plane.
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Autor:
Iturbide, Paula
Alonso-Suárez, Rodrigo
Ronchetti, Franco
Alonso-Suárez, Rodrigo
Ronchetti, Franco
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Tipo de documento:
Preprint
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Palabras clave:
Solar radiation
Machine learning
Satellite images
GOES-16
Machine learning
Satellite images
GOES-16
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Año de publicación:
2023
Contenido:
Resumen:
Accurate solar resource information is a fundamental requirement for solar energy ventures. The lack of precision in solar radiation data can significantly affect the success of the projects. Argentina has solar radiation ground measurement networks. The information obtained through this method is limited due to its spatial sparsity, since it is only possible to measure with appropriate quality in some sites across the territory. To overcome this limitation, it is common to generate models capable of estimating solar
radiation through satellite images, which provide spatial resolution. This work develops
and validates an empirical model for this purpose based on Machine Learning (ML),
demonstrating that it is a useful and accurate tool to be considered. This allows ventures
that make use of this type of energy to have greater certainty in the availability of the
resource, and therefore in the decision-making process. Variables obtained from images of
the geostationary meteorological satellite GOES-16, McClear clear-sky model estimates,
and geometrically calculated information are used as input to the algorithms. The results
of the ML models are compared with estimates from pre-existing models for the region
that incorporate physical modelings, such as Heliosat-4 and CIM-ESRA. The evaluation
shows a higher performance of the ML methods when multi-scale satellite information is
used as input. The incorporation of multi-scale satellite data is not yet implemented in
solar radiation physical modeling, which is an advantage of ML modeling.
Descripción:
Publicado en Proceedings of the XI Conference on Cloud Computing, Big Data & Emerging Topics, La Plata, Argentina. Communications in Computer and Information Science, Vol. 1828, Springer, 2023.
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Citación:
Iturbide, P., Alonso-Suárez, R. y Ronchetti, F. An analysis of satellite-based Machine Learning models to estimate global solar irradiance at a horizontal plane [Preprint]. Publicado en: Cloud Computing, Big Data & Emerging Topics. JCC-BD&ET 2023. Communications in Computer and Information Science, vol 1828. 10 p. DOI: https://doi.org/10.1007/978-3-031-40942-4_9.
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Licencia:
Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
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| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| IAR23.pdf | Preprint | 1010.48 KB | Adobe PDF |