A Novel Self-Adaptive Wind Speed Prediction Model Considering Atmospheric Motion and Fractal Feature

Many of the previous investigations predicted wind speed by directly using wind speed data, which rarely considered physical characteristics of wind speed and was difficult to improve prediction accuracy further. Therefore, a novel self-adaptive wind speed prediction model considering atmospheric motion and fractal feature is developed in this paper. Lorenz-Stenflo (LS) equation is employed to describe the disturbances and chaos effect caused by atmospheric motion on wind speed. One-dimension LS motion series obtained by LS equation is adopted to improve the decomposition effect of wind speed by ensemble empirical mode decomposition (EEMD). The fractal feature of wind speed series is primitively adopted to determine the key parameter in LS equation. Then back propagation (BP) neural network model optimized by genetic algorithm (GA), as a fundamental prediction model, is used for prediction. Eight groups of wind speed series on different time scales from two wind farms are tested and evaluated. The proposed model effectively overcomes the disturbances of atmospheric motion and achieves promising prediction accuracy. Meanwhile, the criterion based on fractal feature ensures accurate selection of the key parameter in atmospheric motion equation according to different features of sampled wind data.