Improving solar forecasting using Deep Learning and Portfolio Theory integration

Solar energy has been consolidated as one of the main renewable energy sources capable of contributing to supply global energy demand. However, the solar resource has intermittent feature in electricity production, making it difficult to manage the electrical system. Hence, we propose the applicatio...

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Detalhes bibliográficos
Autor principal: Lima, Marcello Anderson Ferreira Batista (author)
Outros Autores: Carvalho, Paulo Cesar Marques de (author), Fernández Ramírez, Luis Miguel (author), Braga, Arthur Plínio de Souza (author)
Formato: article
Idioma:por
Publicado em: 2022
Assuntos:
Solar forecast
Artificial intelligence
Deep learning
Portfolio theory
Solar energy
Texto completo:https://doi.org/LIMA, Marcello Anderson Ferreira Batista; CARVALHO, Paulo Cesar Marques de; FERNÁNDEZ RAMÍREZ, Luis Miguel; BRAGA, Arthur Plínio de Souza. Improving solar forecasting using Deep Learning and Portfolio Theory integration. Energy, v. 195, p. 117016, 2020. https://doi.org/10.1016/j.energy.2020.117016
https://doi.org/10.1016/j.energy.2020.117016
País:Brasil
Oai:oai:www.repositorio.ufc.br:riufc/64548

Texto Completo

https://doi.org/LIMA, Marcello Anderson Ferreira Batista; CARVALHO, Paulo Cesar Marques de; FERNÁNDEZ RAMÍREZ, Luis Miguel; BRAGA, Arthur Plínio de Souza. Improving solar forecasting using Deep Learning and Portfolio Theory integration. Energy, v. 195, p. 117016, 2020. https://doi.org/10.1016/j.energy.2020.117016
https://doi.org/10.1016/j.energy.2020.117016

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