SBBD

Paper Registration

1

Select Book

2

Select Paper

3

Fill in paper information

4

Congratulations

Fill in your paper information

English Information

(*) To change the order drag the item to the new position.

Authors
# Name
1 Danielle Rodrigues(danielle.pinna@eic.cefet-rj.br)
2 Diego Brandão(diego.brandao@cefet-rj.br)
3 Rodrigo Franco(rfrancotoso@gmail.com)
4 Kele Belloze(kele.belloze@cefet-rj.br)
5 Raphael Pereira de Oliveira(rguerra@ic.uff.br)
6 Fernando Pereira Gonçalves(fernando.sa@eic.cefet-rj.br)

(*) To change the order drag the item to the new position.

Reference
# Reference
1 ABEEólica (2022). Abeeólica - Associação Brasileira de Energia Eólica. https://abeeolica.org.br/, last accessed on 01/05/22.
2 Blanco, M. A. et al. (2017). Impact of target variable distribution type over the regression analysis in wind turbine data. IWOBI 2017 - Proceedings.
3 EDP (2021). EDP - Open Data. https://opendata.edp.com/pages/ homepage/, last accessed on 15/08/21.
4 Garan, M. et al. (2022). A data-centric machine learning methodology: Application on predictive maintenance of wind turbines. Energies, 15:826.
5 Japa, L. et al. (2023). A population-based hybrid approach for hyperparameter optimization of neural networks. IEEE Access, 11:50752–50768.
6 Mendes, M. et al. (2020). Wind farm and resource datasets: A comprehensive survey and overview. Energies, 13.
7 Pandit, R. et al. (2023). Scada data for wind turbine data-driven condition/performance monitoring: A review on state-of-art, challenges and future trends. Wind Engineering, 47(2):422–441.
8 Qin, S. et al. (2017). Ensemble learning-based wind turbine fault prediction method with adaptive feature selection. Comm. in Computer and Information Science, 728.
9 Soper, D. S. (2023). Hyperparameter optimization using successive halving with greedy cross validation. Algorithms, 16(1).
10 Stetco, A. et al. (2019). Machine learning methods for wind turbine condition monitoring: A review. Renewable energy, 133:620–635.