Conference: ESANN 2022 Proceedings

Wind power forecasting based on bagging extreme learning machine ensemble model

Abstract

The wind energy forecast is an useful tool for wind farm production planning, and operation, facilitating decision making in terms of maintenance, electricity market clearing, and load sharing. This study proposes a cooperative ensemble learning model, using time series pre-processing, multi-objective optimization, and artificial intelligence to forecast wind energy generation in two wind farms in Brazil. Multi-objective optimization is employed to combine variational mode decomposition-based components of a model with bootstrap aggregation (bagging) and extreme learning machine models. Forecasting accuracy is evaluated through the root mean squared error, mean absolute error, mean absolute percentage error, and Diebold-Mariano hypothesis test. The empirical results suggest that proposed ensemble learning model achieved better forecasting performance than bootstrap stacking, machine learning, artificial neural networks, and statistical models, with values of approximately 12.76%, 25.25%, 31.91%, and 34.76%, respectively, in terms of root mean squared errors reduction for out-of-sample forecasting.

DOI Poster Slides

NoteHow to cite this work

Ribeiro, Matheus Henrique Dal Molin, Sinvaldo Rodrigues Moreno, Ramon Gomes da Silva, et al. 2022. “Wind Power Forecasting Based on Bagging Extreme Learning Machine Ensemble Model.” ESANN 2022 Proceedings, 345–50. https://doi.org/10.14428/esann/2022.ES2022-117.

@inproceedings{ribeiro2022esann,
  doi = {10.14428/esann/2022.es2022-117},
  year = {2022},
  publisher = {Ciaco - i6doc.com},
  author = {Matheus Henrique Dal Molin Ribeiro and Sinvaldo Rodrigues Moreno and Ramon Gomes da Silva and Jos{\'{e}} Henrique Kleinubing Larcher and Cristiane Canton and Viviana Cocco Mariani and Leandro dos Santos Coelho},
  title = {Wind power forecasting based on bagging extreme learning machine ensemble model},
  booktitle = {{ESANN} 2022 proceedings}
}