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Variational mode decomposition and bagging extreme learning machine with multi-objective optimization for wind power forecasting

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Abstract

A wind power forecast is an useful support tool for planning and operating wind farm production, facilitating decisions regarding maintenance and load share. This paper presents an evaluation of a cooperative method, which uses a time series pre-processing strategy, artificial neural networks, and multi-objective optimization to forecast wind power generation. The proposed approach also evaluates the accuracy of the hybridization of variational mode decomposition (VMD) with bootstrap aggregation and extreme learning machine model for forecasting very short and short-term wind power generation. Multi-objective strategy aggregates the VMD-based components and obtains the final forecasting. The results imply that the presented algorithm has better forecasting performance compared to bootstrap stacking, other machine learning approaches, and statistical models, with a reduction of root mean squared error of approximately 12.76%, 25.25%, 31.91%, and 34.76%, respectively, for out-of-sample predictions. The forecasting results indicate that the presented approach can improve generalizability and accuracy in cases of very short and short-term wind energy generation.

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Authors and Affiliations

  1. Federal University of Technology - Parana, Pato Branco, Brazil

    Matheus Henrique Dal Molin Ribeiro

  2. Industrial and Systems Engineering Graduate Program, Pontifical Catholic University of Parana, Curitiba, Brazil

    Ramon Gomes da Silva & Leandro dos Santos Coelho

  3. Mechanical Engineering Graduate Program, Pontifical Catholic University of Parana, Curitiba, Brazil

    Sinvaldo Rodrigues Moreno, José Henrique Kleinübing Larcher & Viviana Cocco Mariani

  4. Pato Branco University Center, Pato Branco, Brazil

    Cristiane Canton

  5. Digital Industry Center, Fondazione Bruno Kessler, Trento, Italy

    Stefano Frizzo Stefenon

  6. Department of Mathematics, Computer Science and Physics, University of Udine, Udine, Italy

    Stefano Frizzo Stefenon

  7. Department of Electrical Engineering, Federal University of Parana, Curitiba, Brazil

    Viviana Cocco Mariani & Leandro dos Santos Coelho

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  1. Matheus Henrique Dal Molin Ribeiro
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  2. Ramon Gomes da Silva
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Contributions

Writing - original draft preparation: Matheus Henrique Dal Molin Ribeiro; Conceptualization: Ramon Gomes da Silva; Methodology: Sinvaldo Rodrigues Moreno; Formal analysis and investigation: Cristiane Canton; Writing - review and editing: José Henrique Kleinübing Larcher and Stefano Frizzo Stefenon; Supervision: Viviana Cocco Mariani and Leandro dos Santos Coelho.

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Appendix

Appendix

1.1 A Hyperparameters

Table 7 shows hyperparameters for the forecasting methods. The hyperparameter’s tuning for each model was performed using a grid-search strategy over the cross-validation procedure in the time series. It adopted the definitions provided by caret package by RMSE minimization considering the training set by the use of train function. In this regard, using the tuneLenght argument, the elements for the search space were specified. In this specification, it was taken into consideration that a lower result for tuneLenght may not be enough to produce an acceptable prediction result, and a higher value for tuneLenght may increase the time for training the model.

For ELM the hyperparameters are the number of hidden neurons (a random value between 1 and 20). Also, the distribution from which the input weights and the bias should be initialized, as well as the activation function were randomly selected between those available in the elmNNRcpp package [65] from R software. For the ANFIS, LSTM, GRU, RNN, and CNN models, the hyperparameters were also set by grid search, considering that a higher number of epochs and hidden layers may lead to overfitting and higher computing effort, whereas lower values may be not sufficient to reach acceptable results. For the statistical approaches, the selection is further automated following the definitions of the forecast package [48] based on the Akaike Information Criterion. Lastly, the NSGA-II and TOPSIS were determined based on Ribeiro et al. [66].

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Ribeiro, M.H.D.M., da Silva, R.G., Moreno, S.R. et al. Variational mode decomposition and bagging extreme learning machine with multi-objective optimization for wind power forecasting. Appl Intell 54, 3119–3134 (2024). https://doi.org/10.1007/s10489-024-05331-2

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