Short-Term Wind Speed Forecasting Using a Multi-model Ensemble
Reliable and accurate short-term wind speed forecasting is of great importance for secure power system operations. In this study, a novel two-step method to construct a multi-model ensemble, which consists of linear regression, multi-layer perceptrons and support vector machines, is proposed. The ensemble members first compete with each other in a number of training rounds, and the one with the best forecasting accuracy in each round is recorded. Then, after all the training rounds, the occurrence frequency of each member is calculated and used as the weight to form the final multi-model ensemble. The effectiveness of the proposed multi-model ensemble has been assessed on the real datasets collected from three wind farms in China. The experimental results indicate that the proposed ensemble is capable of providing better performance than the single predictive models composing it.
KeywordsWind speed forecasting Model combination Ensemble Linear regression Multi-layer perceptron Support vector machine
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- 6.Welch, R.L., Ruffing, S.M., Venayagamoorthy, G.K.: Comparison of feedforward and feedback neural network architectures for short term wind speed prediction. In: International Joint Conference on Neural Networks, pp. 3335–3340. IEEE, Atlanta (2009)Google Scholar
- 7.Alanis, A.Y., Ricalde, L.J., Sanchez, E.N.: High Order Neural Networks for wind speed time series prediction. In: International Joint Conference on Neural Networks, pp. 76–80. IEEE, Atlanta (2009)Google Scholar
- 9.Zhao, P., Xia, J., Dai, Y., He, J.: Wind speed prediction using support vector regression. In: 5th IEEE Conference on Industrial Electronics and Applications, pp. 882–886. IEEE, Taichung (2010)Google Scholar
- 11.Heinermann, J., Kramer, O.: Precise wind power prediction with SVM ensemble regression. In: Wermter, S., Weber, C., Duch, W., Honkela, T., Koprinkova-Hristova, P., Magg, S., Palm, G., Villa, A.E.P. (eds.) ICANN 2014. LNCS, vol. 8681, pp. 797–804. Springer, Heidelberg (2014)Google Scholar
- 12.Barrow, D.K., Crone, S.F., Kourentzes, N.: An evaluation of neural network ensembles and model selection for time series prediction. In: International Joint Conference on Neural Networks, pp. 1–8. IEEE, Barcelona (2010)Google Scholar
- 14.Wei, H.K.: Theory and Methods for Neural Networks Architecture Design. National Defence Industry Press, Beijing (2005)Google Scholar
- 15.Haykin, S.S.: Neural networks and learning machines. Pearson Education, Upper Saddle River (2009)Google Scholar
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