A WIND SPEED FORECASTING METHOD USING A GAUSSIAN PROCESS REGRESSION MODEL CONSIDERING DATA UNCERTAINTY

Wind speed forecasting has become an essential part of power forecasting, daily operation, and optimal scheduling of wind farms. However, due to the extreme randomness and unpredictability of wind resources, it's still a very challenging task to accurately forecast wind speed considering data uncertainties. Most existing methods do not take into account the data uncertainty and randomness in wind speed forecasting, resulting in inaccurate results in practical applications. This paper proposes a hybrid intelligent model for wind speed forecasting under uncertainties by adeptly integrating Bayesian Discrete Wavelet Packet Transform (BDWPT) and Gaussian Process Regression (GPR). Firstly, the BDWPT method is applied to reduce the noise and randomness of raw data by taking advantage of its powerful adaptive denoising capability. Then, the GPR model is developed to model the randomness in wind speed forecasting. Finally, a comparison study with traditional methods by using the data collected from real-world wind farms is conducted to show the advantages of the proposed methodology in terms of one-step and multi-step cases. This study provides a promising approach to accurately forecast wind speed for turbine design and power management considering data uncertainties.