Medium and Short-Term Wind Power Forecasting Using LCNN-Mamba Model Based on Quadratic Decomposition

Abstract

Accurate wind power forecasting is essential for advancing sustainable wind energy utilization, stabilizing new energy power systems, and facilitating the low-carbon and sustainable development of the power industry. Aiming to address the challenges of strong non-stationarity, significant multiscale fluctuations, and the difficulty in jointly modeling local dynamics and long-term dependencies in wind power time series, this study proposes a short-to-medium-term wind power forecasting method based on multiscale decomposition and local-global cooperative modeling. First, Seasonal Trend Decomposition (STL) is employed to extract the trend and seasonal components from the raw time series. The residual series is then subjected to Adaptive Noise-Complemented Empirical Mode Decomposition (CEEMDAN) to reveal the underlying non-stationary characteristics. Based on the dominant frequency characteristics, the decomposed components are reconstructed into high, medium, and low-frequency series, which, together with the trend and seasonal components, form a multidimensional set of input features. Second, to accommodate the time-series characteristics of the multiscale components, we designed an LCNN-Mamba forecasting framework that integrates a Local Convolutional Neural Network (LocalCNN) with a state-space model. The LocalCNN is used to model local and multiscale dynamic features, while the multi-layer Mamba state-space module efficiently captures long-term time-series dependencies, thereby achieving a synergistic enhancement of both local sensitivity and global modeling capabilities. The proposed model was validated using actual operational data from wind farms at two different locations. Experimental results show that the proposed approach outperforms traditional forecasting methods in terms of forecasting accuracy, providing a reliable technical reference for wind power grid-connected operation, power dispatching optimization, and the large-scale and high-quality sustainable development of wind energy resources.