Transformer Neural Network Architecture for Forecasting of Colombian Solar Irradiance

Producción científica: Capítulo en Libro/InformeContribución a la conferencia

Resumen

Renewable resources for electrical energy generation are each time more demanded. Solar irradiation is widely used on these days to compute the possible energy generation. However, the current climate change makes the measuring of the availability of this supply a challenge. For this, forecasting models can be employed to determine what so convenient could be the projection of the generation. This paper shows an approach based on comparison of two neural networks architecture for forecasting of solar irradiance, which can be a resource for photovoltaic generation. Long short-term memory and transformer models were analyzed for determine what network holds better performance in this specific case. Information from three days and a transformer neural network with eight heads presented the best result for the forecasting.

Idioma originalInglés estadounidense
Título de la publicación alojada2024 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2024 - Proceedings
EditoresAlvaro David Orjuela-Canon
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9798331516901
DOI
EstadoPublicada - 2024
Evento2024 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2024 - Pamplona, Colombia
Duración: jul. 17 2024jul. 19 2024

Serie de la publicación

Nombre2024 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2024 - Proceedings

Conferencia

Conferencia2024 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2024
País/TerritorioColombia
CiudadPamplona
Período7/17/247/19/24

Áreas temáticas de ASJC Scopus

  • Inteligencia artificial
  • Informática aplicada
  • Control y optimización

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