Big data driven multi-objective predictions for offshore wind farm based on machine learning algorithms

This paper explores the big data driven multi-objective predictions for offshore wind farm based on machine learning. A data-driven prediction framework is proposed to predict the wind farm power output and structural fatigue. Unlike the existing methods that are normally based on analytical models, mainly focus on single objective and ignore the control contributions, the proposed framework uses the turbine control inputs, inflow wind velocity and directions as the predictor variables. It is constructed by training five typical machine learning approaches: the general regression neural network (GRNN), random forest (RF), support vector machine (SVM), gradient boosting regression (GBR) and recurrent neural network (RNN). The assessment of these approaches is based on the FLOw Redirection and Induction in Steady State (FLORIS) under 6 different scenarios. The test results in different cases are highly consistent with each other and validate that very minor accuracy differences exist among these approaches and they all can achieve the relative accuracy of around 99% or more, which is sufficiently accurate for practical applications. The RNN and SVM exhibit the best accuracy, and particularly the RNN has the best accuracy in thrust predictions. The results also demonstrate that the GRNN has the best computational efficiency.