Abstract
In this work we will apply Diffusion Maps (DM), a recent technique for dimensionality reduction and clustering, to build local models for wind energy forecasting. We will compare ridge regression models for K–means clusters obtained over DM features, against the models obtained for clusters constructed over the original meteorological data or principal components, and also against a global model. We will see that a combination of the DM model for the low wind power region and the global model elsewhere outperforms other options.
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References
Alaíz, C., Barbero, A., Fernández, A., Dorronsoro, J.: High wind and energy specific models for global production forecast. In: Proceedings of the European Wind Energy Conference and Exhibition (EWEC 2009), Marseille, France (March 2009)
Barbero, A., López, J., Dorronsoro, J.: Kernel methods for wide area wind power forecasting. In: Proceedings of the European Wind Energy Conference and Exhibition (EWEC 2008), Brussels, Belgium (April 2008)
Belkin, M., Nyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6), 1373–1396 (2003)
Bengio, Y., Delalleau, O., Roux, N.L., Paiement, J., Vincent, P., Ouimet, M.: Learning eigenfunctions links spectral embedding and kernel pca. Neural Computation 16(10), 2197–2219 (2004)
Coifman, R., Lafon, S.: Diffusion maps. Applied and Computational Harmonic Analysis 21(1), 5–30 (2006)
European center for medium–range weather forecasts (2005), http://www.ecmwf.int/
Hoerl, A.E., Kennard, R.W.: Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12(12), 55–67 (1970)
Monteiro, C., Bessa, R., Miranda, V., Botterud, A., Wang, J., Conzelmann, G.: Wind power forecasting: State–of–the–art 2009. Tech. rep., INESC Porto and Argonne National Laboratory (2009)
Pinson, P., Nielsen, H., Madsen, H., Nielsen, T.: Local linear regression with adaptive orthogonal fitting for the wind power application. Statistics and Computing 18(1), 59–71 (2009)
Rabin, N., Coifman, R.: Heterogeneous datasets representation and learning using diffusion maps and laplacian pyramids. In: Proceedings of the 12th SIAM International Conference on Data Mining (SDM 2012), Anaheim, California, USA (April 2012)
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Fernández Pascual, Á., Alaíz, C.M., González Marcos, A.M., Díaz García, J., Dorronsoro, J.R. (2012). Diffusion Maps and Local Models for Wind Power Prediction. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33266-1_70
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DOI: https://doi.org/10.1007/978-3-642-33266-1_70
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