PNNL

Abstract

Abstract. Model uncertainty is a significant challenge in the wind energy industry and can lead to mischaracterization of millions of dollars’ worth of wind resource. Machine learning methods, notably deep artificial neural networks (ANNs), are capable of modeling turbulent and chaotic systems and offer a promising tool to produce high-accuracy wind speed forecasts and extrapolations. This paper uses data collected by profiling Doppler lidars over three field campaigns to investigate the efficacy of using ANNs for wind speed vertical extrapolation in a variety of terrains, and quantifies the role of domain knowledge on ANN extrapolation accuracy. A series of 11 meteorological parameters (features) are used as ANN inputs and the resulting output accuracy is compared with that of both standard log law and power law extrapolations. It is found that extracted non-dimensional inputs, namely turbulence intensity, current wind speed, and previous wind speed, are the features that most reliably improve the ANN’s accuracy, providing up to a 65% and 52% increase in extrapolation accuracy over log law and power law predictions, respectively. The volume of input data is also deemed important for achieving robust results. One test case is analyzed in-depth using dimensional and non-dimensional features, showing that feature non-dimensionalization drastically improves network accuracy and robustness for sparsely sampled atmospheric cases.