Abstract
The measure-correlate-predict technique is state-of-the-art for assessing the quality of a wind power resource based on long term numerical weather prediction systems. On-site wind speed measurements are correlated to meteorological reanalysis data, which represent the best historical estimate available for the atmospheric state. The different variants of MCP more or less correct the statistical main attributes by making the meteorological reanalyses bias and scaling free using the on-site measurements. However, by neglecting the higher order correlations none of the variants utilize the full potential of the measurements. We show that deep neural networks make use of these higher order correlations. Our implementation is tailored to the requirements of MCP in the context of wind resource assessment. We show the application of this method to a set of different locations and compare the results to a simple linear fit to the wind speed frequency distribution as well as to a standard linear regression MCP, that represents the state-of-the-art in industrial aerodynamics. The neural network based MCP outperforms both other methods with respect to correlation, root-mean-square error and the distance in the wind speed frequency distribution. Site assessment can be considered one of the most important steps developing a wind energy project. To this end, the approach described can be regarded as a novel, high-quality tool for reducing uncertainties in the long-term reference problem of on-site measurements.
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References
Rogers, A.L., Rogers, J.W., Manwell, J.F.: Comparison of the performance of four measure–correlate–predict algorithms. Journal of Wind Engineering and Industrial Aerodynamics 93(3), 243–264 (2005)
Bengio, Y.: Learning deep architectures for ai. Foundations and Trends® in Machine Learning 2(1), 1–127 (2009)
Rienecker, M.M., Suarez, M.J., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., Bosilovich, M.G., Schubert, S.D., Takacs, L., Kim, G.K., et al.: MERRA: NASA’s modern-era retrospective analysis for research and applications. Journal of Climate 24(14), 3624–3648 (2011)
Howard, T., Clark, P.: Correction and downscaling of NWP wind speed forecasts. Meteorological Applications 14(2), 105–116 (2007)
Meis, J., Kuntze, K.: Wind potential analysis for great heights with archived GFS data. In: European Wind Energy Conference and Exhibition, vol. PO. ID 173 (2010)
Sack, J., Strunk, A., Meis, J., Sehnke, F., Felder, M.D., Kaifel, A.K.: From ensembles to probabilistic wind power forecasts - how crucial is the ensemble size? In: Proceedings of the 2nd International Conference on Energy and Meteorology, ICEM, Toulouse (2013)
Ng, A.Y.: Feature selection, l 1 vs. l 2 regularization, and rotational invariance. In: Proceedings of the Twenty-First International Conference on Machine Learning, p. 78. ACM (2004)
Felder, M.D., Sehnke, F., Kaifel, A.K.: Automatic feature selection for combined iasi/gome-2 ozone profile retrieval. In: Proceedings of the 2012 EUMETSAT Meteorological Satellite Conference (2012)
Sehnke, F., Osendorfer, C., Rückstieß, T., Graves, A., Peters, J., Schmidhuber, J.: Parameter-exploring policy gradients. Neural Networks 23(4), 551–559 (2010)
Sehnke, F., Graves, A., Osendorfer, C., Schmidhuber, J.: Multimodal parameter-exploring policy gradients. In: 2010 Ninth International Conference on Machine Learning and Applications, ICMLA, pp. 113–118. IEEE (2010)
Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: The rprop algorithm. In: IEEE International Conference on Neural Networks 1993, pp. 586–591. IEEE (1993)
Salakhutdinov, R., Mnih, A., Hinton, G.: Restricted boltzmann machines for collaborative filtering. In: ACM International Conference Proceeding Series, vol. 227, pp. 791–798 (2007)
Martens, J.: Deep learning via hessian-free optimization. In: Proceedings of the 27th International Conference on Machine Learning, ICML, vol. 951 (2010)
Sutskever, I.: Training Recurrent Neural Networks. PhD thesis, University of Toronto (2013)
Sehnke, F., Felder, M.D., Kaifel, A.K.: Learn-o-matic: A fully automated machine learning suite for profile retrieval applications. In: Proceedings of the 2012 EUMETSAT Meteorological Satellite Conference (2012)
Mnih, V.: Cudamat: a cuda-based matrix class for python. Department of Computer Science, University of Toronto. Tech. Rep. UTML TR 4 (2009)
Tieleman, T.: Gnumpy: an easy way to use gpu boards in python. Department of Computer Science, University of Toronto (2010)
Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012)
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Sehnke, F., Strunk, A., Felder, M., Brombach, J., Kaifel, A., Meis, J. (2013). Wind Power Resource Estimation with Deep Neural Networks. In: Mladenov, V., Koprinkova-Hristova, P., Palm, G., Villa, A.E.P., Appollini, B., Kasabov, N. (eds) Artificial Neural Networks and Machine Learning – ICANN 2013. ICANN 2013. Lecture Notes in Computer Science, vol 8131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40728-4_70
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DOI: https://doi.org/10.1007/978-3-642-40728-4_70
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