Abstract
Two problems when using Numerical Weather Prediction features in Machine Learning are the high dimensionality inherent to the current high-resolution models, and the high correlation of the features, which can affect the performance of learning machines as Multilayer Perceptron (MLP). In this work we propose to reduce the dimension of the problem using a supervised Fused Lasso model, which generates meta-features corresponding to the average of the groups with constant coefficients. The Fused Lasso problem is defined in terms of the feature correlation graph and tries to retain features with the stronger connections. As shown experimentally, training the models over the correlation graph-based reduced dataset allows to decrease the overall computational time while preserving almost the same error in the case of Support Vector Regressors and even improving the error of the MLPs, if the original dimension is high.
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Acknowledgements
With partial support from Spain’s grants TIN2016-76406-P and S2013/ICE-2845 CASI-CAM-CM. Work supported also by project FACIL–Ayudas Fundación BBVA a Equipos de Investigación Científica 2016, and the UAM–ADIC Chair for Data Science and Machine Learning. We thank Red Eléctrica de España for making available wind energy data and gratefully acknowledge the use of the facilities of Centro de Computación Científica (CCC) at UAM. We also thank the Agencia Española de Meteorología, AEMET, and the ECMWF for access to the MARS repository.
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Catalina, A., Alaíz, C.M., Dorronsoro, J.R. (2018). Fused Lasso Dimensionality Reduction of Highly Correlated NWP Features. In: Woon, W., Aung, Z., Catalina Feliú, A., Madnick, S. (eds) Data Analytics for Renewable Energy Integration. Technologies, Systems and Society. DARE 2018. Lecture Notes in Computer Science(), vol 11325. Springer, Cham. https://doi.org/10.1007/978-3-030-04303-2_2
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