Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Jonata C. de Albuquerque"'
Autor:
Jonata C. de Albuquerque, Ronaldo R. B. de Aquino, Otoni Nobrega Neto, Milde M. S. Lira, Aida A. Ferreira, Manoel Afonso de Carvalho
Publikováno v:
Journal of Modern Power Systems and Clean Energy, Vol 9, Iss 3, Pp 526-533 (2021)
We propose a new way to develop non-parametric models of power curves using artificial intelligence tools. One parametric model and eight non-parametric models are developed to emulate the behavior described by the power curve of the wind farms. A co
Externí odkaz:
https://doaj.org/article/ccb465667c174fc7b78379814b929509
Autor:
Hugo T. V. Gouveia, Murilo A. Souza, Aida A. Ferreira, Jonata C. de Albuquerque, Otoni Nóbrega Neto, Milde Maria da Silva Lira, Ronaldo R. B. de Aquino
Publikováno v:
Energies, Vol 16, Iss 6, p 2635 (2023)
The large-scale integration into electrical systems of intermittent power-generation sources, such as wind power plants, requires greater efforts and knowledge from operators to keep these systems operating efficiently. These sources require reliable
Externí odkaz:
https://doaj.org/article/2d55b6eb1eaf42c2b461780579db3827
Autor:
Ronaldo R. B. de Aquino, Milde M. S. Lira, Jonata C. de Albuquerque, Aida A. Ferreira, Otoni Nobrega Neto, Manoel A. Carvalho
Publikováno v:
Journal of Modern Power Systems and Clean Energy, Vol 9, Iss 3, Pp 526-533 (2021)
This paper proposes a new way of developing non-parametric models of power curves, using artificial intelligence tools. Here, nine models are developed, one being parametric and eight non-parametric, to emulate the behavior dictated by the power curv
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d672eb71786d4ba1fe783633ec0ac150
https://ieeexplore.ieee.org/document/9214975/
https://ieeexplore.ieee.org/document/9214975/
Autor:
Helen Barboza da Silva, Alcides Codeceira Neto, Aida A. Ferreira, Manuel Herrera, Jonata C. de Albuquerque, Ronaldo R. B. de Aquino
Publikováno v:
2017 International Conference on Computational Science and Computational Intelligence (CSCI).
The high cost of energy production, coupled with the advantages of wind power as renewable and widely available source of energy, has led several countries to establish incentives to regulate and promote wind power generation. This work proposes the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2c96695d629e778f2a354ba1367ad32b
https://doi.org/10.1109/csci.2017.262
https://doi.org/10.1109/csci.2017.262
Autor:
Aida A. Ferreira, Alcides Codeceira Neto, Milde M. S. Lira, Ronaldo R. B. de Aquino, Otoni Nobrega Neto, Jonata C. de Albuquerque
Publikováno v:
2017 International Conference on Computational Science and Computational Intelligence (CSCI).
This paper, proposes the use of Deep Learning in predictive nonparametric models that use artificial intelligence tools to approximate power curves of wind farms. Three different tools are evaluated: artificial neural networks, fuzzy inference system
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ca7a26b5761ce1b1eb474f012d988c96
https://doi.org/10.1109/csci.2017.144
https://doi.org/10.1109/csci.2017.144
Autor:
Aida A. Ferreira, Manoel A. Carvalho, Otoni Nobrega Neto, Jonata C. de Albuquerque, Alcides Codeceira Neto, Ronaldo R. B. de Aquino, Milde M. S. Lira
Publikováno v:
IJCNN
The modeling of wind power curve is important in turbine performance monitoring and in wind power forecasting. There are several techniques to fit the power curve of a wind turbine, which can be classified into parametric and nonparametric methods. T
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dddc35f8982b383021e2e5ed0e5e2b8c
https://doi.org/10.1109/ijcnn.2016.7727707
https://doi.org/10.1109/ijcnn.2016.7727707