Ensemble Learning Models for Wind Power Forecasting-Reference-Cited by-同舟云学术

Ensemble Learning Models for Wind Power Forecasting

Published:2024 Issue: Volume: Page:15-27
ISSN:2194-5357
Container-title:Advances in Intelligent Systems and Computing
language:en
Short-container-title:

Author:

Deon Samara,de Lima José Donizetti,Dranka Geremi Gilson,Ribeiro Matheus Henrique Dal Molin,dos Anjos Julio Cesar Santos,de Paz Santana Juan Francisco,Leithardt Valderi Reis Quietinho

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-66635-3_2

Reference31 articles.

1. da Silva, R.G., Moreno, S.R., Ribeiro, M.H.D.M., Larcher, J.H.K., Mariani, V.C., dos Santos Coelho, L.: Multi-step short-term wind speed forecasting based on multi-stage decomposition coupled with stacking-ensemble learning approach. Int. J. Electr. Power Energy Syst. 143, 108504 (2022). https://doi.org/10.1016/j.ijepes.2022.108504

2. Stefenon, S.F., Seman, L.O., Aquino, L.S., dos Santos Coelho, L.: Wavelet-Seq2Seq-LSTM with attention for time series forecasting of level of dams in hydroelectric power plants. Energy 274, 127350 (2023). https://doi.org/10.1016/j.energy.2023.127350

3. Yamasaki, M., Freire, R.Z., Seman, L.O., Stefenon, S.F., Mariani, V.C., dos Santos Coelho, L.: Optimized hybrid ensemble learning approaches applied to very short-term load forecasting. Int. J. Electr. Power Energy Syst. 155, 109579 (2024). https://doi.org/10.1016/j.ijepes.2023.109579

4. Starke, L., Hoppe, A.F., Sartori, A., Stefenon, S.F., Santana, J.F.D.P., Leithardt, V.R.Q.: Interference recommendation for the pump sizing process in progressive cavity pumps using graph neural networks. Sci. Rep. 13(1), 16884 (2023). https://doi.org/10.1038/s41598-023-43972-4

5. Corso, M.P., Stefenon, S.F., Singh, G., Matsuo, M.V., Perez, F.L., Leithardt, V.R.Q.: Evaluation of visible contamination on power grid insulators using convolutional neural networks. Electr. Eng. 105, 3881–3894 (2023). https://doi.org/10.1007/s00202-023-01915-2