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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Bibliographic Details
Main Author: Lima, Marcello Anderson Ferreira Batista (author)
Other Authors: Carvalho, Paulo Cesar Marques de (author), Fernández Ramírez, Luis Miguel (author), Braga, Arthur Plínio de Souza (author)
Format: article
Language:por
Published: 2022
Subjects:
Solar forecast
Artificial intelligence
Deep learning
Portfolio theory
Solar energy
Online Access: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
Country:Brazil
Oai:oai:www.repositorio.ufc.br:riufc/64548

Internet

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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