Articles | Volume 6, issue 1
Wind Energ. Sci., 6, 295–309, 2021
Wind Energ. Sci., 6, 295–309, 2021
Research article
01 Mar 2021
Research article | 01 Mar 2021

Utilizing physics-based input features within a machine learning model to predict wind speed forecasting error

Daniel Vassallo et al.

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Cited articles

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, 22.9, 1165–1174, 2019. a
Bianco, L., Djalalova, I. V., Wilczak, J. M., Olson, J. B., Kenyon, J. S., Choukulkar, A., Berg, L. K., Fernando, H. J. S., Grimit, E. P., Krishnamurthy, R., Lundquist, J. K., Muradyan, P., Pekour, M., Pichugina, Y., Stoelinga, M. T., and Turner, D. D.: Impact of model improvements on 80 m wind speeds during the second Wind Forecast Improvement Project (WFIP2), Geosci. Model Dev., 12, 4803–4821,, 2019. a
Bodini, N., Lundquist, J. K., and Optis, M.: Can machine learning improve the model representation of turbulent kinetic energy dissipation rate in the boundary layer for complex terrain?, Geosci. Model Dev., 13, 4271–4285,, 2020. a, b
Box, G. E., Jenkins, G. M., Reinsel, G. C., and Ljung, G. M.: Time series analysis: forecasting and control, John Wiley & Sons, Hoboken, NJ, 2015. a
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001. a
Short summary
Machine learning is quickly becoming a commonly used technique for wind speed and power forecasting and is especially useful when combined with other forecasting techniques. This study utilizes a popular machine learning algorithm, random forest, in an attempt to predict the forecasting error of a statistical forecasting model. Various atmospheric characteristics are used as random forest inputs in an effort to discern the most useful atmospheric information for this purpose.