The importance of atmospheric turbulence and stability in machine-learning models of wind farm power production. (September 2019)

• Record Type: Journal Article
• Authors: Optis, Mike; Perr-Sauer, Jordan
• Is Part Of: Renewable & sustainable energy reviews. Volume 112(2019)
• Page Start: 27
• Page End: 41
• Publication Date: 2019-09
• Subjects: Machine learning -- Data mining -- Wind farm power -- Wind power forecasting -- Atmospheric stability -- Turbulence; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2019.05.031
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: Machine learning is frequently applied in the wind energy industry to build statistical models of wind farm power production using atmospheric data as input. In the field of wind power forecasting, in particular, there has been substantial research into finding the best-performing learning algorithms that improve model predictions. Overlooked in the literature, however, is the influence of atmospheric turbulence and stability measurements in improving model predictions. It has been well-established through observations and physical models that these effects can have considerable influence on wind farm power production; yet consideration of these effects in statistical models is almost entirely absent from the literature. In this work, we examine the impact of atmospheric turbulence and stability inputs on statistical model predictions of wind farm power output. Hourly observations from a wind farm in the Pacific Northwest United States located in very complex terrain are used. Five common learning algorithms and nine atmospheric variables are considered, five of which represent some measure of turbulence or stability. We find a considerable improvement in hourly power predictions when some measure of turbulence or stability is included in the model. In particular, turbulent kinetic energy was found to be the most important variable apart from wind speed and more important than wind direction, pressure, and temperature. By contrast, the choice of learning algorithm is shown to be relatively less important in improving predictions. Based on this work, we recommend that turbulence and stability variables become standard inputs into statistical models of wind farm power production. Highlights: Turbulence and stability are almost never considered in wind plant ML models. All turbulence and stability variable are statistically significant. Turbulent kinetic energy is the most significant variable after wind speed. Choice of variables is more important than choice of algorithm.