A Wind Speed Forecasting Method Using Gaussian Process Regression Model Under Data Uncertainty

Abstract Wind speed forecasting plays a pivotal role in power prediction, daily operations, and optimal scheduling of wind farms. However, accurately predicting wind speed remains challenging due to data uncertainties and the inherent randomness of wind resources. This paper introduces a novel wind speed forecasting method by combining Bayesian discrete wavelet packet thresholding (BDWPT) into Gaussian Process Regression (GPR). The BDWPT method is first employed to adaptively remove noise from wind speed data, retaining the main trend characteristics of the time series while removing redundant information. The GPR model is then utilized to capture the remaining randomness and effectively predict future probabilistic trends in wind speed. Comparative studies using real-world wind farm data demonstrate the advantages of the proposed method in both one-step and multistep forecasting scenarios, showcasing its potential to enhance turbine design and power management under uncertain conditions.