Machine Learning Algorithms for Vertical Wind Speed Data Extrapolation: Comparison and Performance Using Mesoscale and Measured Site Data

Luis Baquero, Herena Torio and Paul Leask
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Luis Baquero: Institute of Physics, Faculty of Mathematics and Science, Carl von Ossietzky University of Oldenburg, 26129 Oldenburg, Germany
Herena Torio: Institute of Physics, Faculty of Mathematics and Science, Carl von Ossietzky University of Oldenburg, 26129 Oldenburg, Germany
Paul Leask: Project Development and Analytics, Energy Systems, DNV, 26129 Oldenburg, Germany

Energies, 2022, vol. 15, issue 15, 1-20

Abstract: Machine learning (ML) could be used to overcome one of the largest sources of uncertainty in wind resource assessment: to accurately predict the wind speed (WS) at the wind turbine hub height. Therefore, this research defined and evaluated the performance of seven ML supervised algorithms (regressions, decision tree, support vector machines, and an ensemble method) trained with meteorological mast data (temperature, humidity, wind direction, and wind speeds at 50 and 75 m), and mesoscale data below 80 m (from the New European Wind Atlas) to predict the WS at the height of 102 m. The results were compared with the conventional method used in wind energy assessments to vertically extrapolate the WS, the power law. It was proved that the ML models overcome the conventional method in terms of the prediction errors and the coefficient of determination. The main advantage of ML over the power-law was that ML performed the task using either only mesoscale data (described in scenario A), only data from the measurement mast (described in scenario B) or combining these two data sets (described in scenario C). The best ML models were the ensemble method in scenario A with an R 2 of 0.63, the linear regression in scenario B with an R 2 of 0.97, and the Ridge regressor in scenario C with an R 2 of 0.97.

Keywords: wind speed extrapolation; power-law; machine learning; supervised learning; mesoscale model; wind energy; energy production assessment; new european wind atlas; random forest; support vector machines; linear regression (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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