Enhancing probabilistic wind speed forecasting by integrating self-adaptive Bayesian wavelet denoising with deep Gaussian process regression under uncertainties

Wind speed forecasting is crucial for wind power prediction, wind farm operations, and power optimization scheduling. However, the inherent randomness and uncontrollability of wind resources make accurate forecasting a significant challenge. Traditional methods often struggle to effectively handle noise and uncertainty, limiting their practical applicability. This paper introduces a hybrid wind speed forecasting model that integrates self-adaptive Bayesian Wavelet Packet Thresholding (BDWPT) and a Deep Gaussian Process (DGP) to enhance prediction accuracy. BDWPT is utilized to adaptively reduce noise while preserving essential time series trends, thereby minimizing input uncertainties. The DGP model is then employed to capture the stochastic nature of wind speed fluctuations and generate probabilistic forecasts. Additionally, Monte Carlo simulation is applied to quantify output uncertainties. The proposed model was validated through a comparison study using real-world data from four wind farms operating under various conditions. Results demonstrate that the hybrid approach significantly outperforms traditional methods, achieving over 90% improvement in forecast accuracy. This method offers a reliable tool for wind power applications, enabling more informed decision-making and enhancing wind farm efficiency.