Articles | Volume 5, issue 3
Wind Energ. Sci., 5, 959–975, 2020

Special issue: Flow in complex terrain: the Perdigão campaigns (WES/ACP/AMT...

Wind Energ. Sci., 5, 959–975, 2020
Research article
26 Jul 2020
Research article | 26 Jul 2020

Decreasing wind speed extrapolation error via domain-specific feature extraction and selection

Daniel Vassallo et al.

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Cited articles

Aggarwal, C. C.: Neural networks and deep learning, Springer, Cham, Switzerland, 2018. a, b
Akish, E., Bianco, L., Djalalova, I. V., Wilczak, J. M., Olson, J. B., Freedman, J., Finley, C., and Cline, J.: Measuring the impact of additional instrumentation on the skill of numerical weather prediction models at forecasting wind ramp events during the first Wind Forecast Improvement Project (WFIP), Wind Energy, 22, 1165–1174, 2019. a
A2e – Atmosphere to Electrons: wfip2/lidar.z03.00, Maintained by A2e Data Archive and Portal for US Department of Energy, Office of Energy Efficiency and Renewable Energy,, 2017. a
Baklanov, A. A., Grisogono, B., Bornstein, R., Mahrt, L., Zilitinkevich, S. S., Taylor, P., Larsen, S. E., Rotach, M. W., and Fernando, H.: The nature, theory, and modeling of atmospheric planetary boundary layers, B. Am. Meteorol. Soc., 92, 123–128, 2011. a
Short summary
Model error and uncertainty is a challenge in the wind energy industry, potentially leading to mischaracterization of millions of dollars' worth of wind resource. This paper combines meteorological knowledge with machine learning techniques, specifically artificial neural networks (ANNs), to better extrapolate wind speeds. It is found that ANNs can reduce power-law extrapolation error by up to 52 % while simultaneously reducing uncertainty. A test case is shown to help decipher the ANN results.