Machine learning for predicting offshore vertical wind profiles

Abstract

The accurate characterization of the vertical wind profile over the sea that covers the rotor swept area of modern wind turbines is a key challenge for wind energy yield calculations. Since offshore wind measurements are scarce, early-phase projects tend to use numerical model outputs before planning a dedicated measurement campaign. This study aims to develop and validate a machine-learning model that can assimilate wind parameters measured at the first level of the meteorological masts as input and provide a wind speed profile covering the rotor swept area of modern turbines that is more accurate than numerical weather prediction models. The methodology is based on a random forest model implemented in the python package Scikit-Learn. Three offshore sites in the North Sea have been selected for this study, namely FINO3, IJmuiden and Nordsee Ost’s (NSO) met mast, which are 100 to 350 km apart. Each site has an instrumented 100-m meteorological mast along with a Doppler wind lidar measuring up to 300 m. The baseline selected for comparison was the Weather Research and Forecast (WRF) model. To assess the accuracy of the random forest model two models were trained at IJmuiden and FINO3 and tested at all three locations. Hence, we examine the model performance at the site of training, the so-called same site approach, as well as new sites, the so-called round robin approach. The same site approach quantifies the model consistency, while the round robin shows the degree of spatial robustness. The results show that the model is consistent, where the model trained and tested at FINO3 showed a mean absolute error (MAE) reduction of 68% compared to WRF. This model is also robust, when applied at IJmuiden, 350 km away with a MAE of 1.2 m/s, 8% improved compared to WRF outputs. This study therefore shows the potential to implement machine-learning methods in the prediction of vertical wind speed profiles over the sea. One potential application for the presented methodology is the extension of wind profiles measured by floating lidar systems to higher heights, where current wind lidar products have low availability and higher associated uncertainties, when measuring at a higher height.