Short-term wind speed forecasting based on adaptive secondary decomposition and robust temporal convolutional network

Accurate and reliable short-term wind speed forecasting remains challenging due to the high volatility and randomness of wind speed. The secondary decomposition (SD) method deeply extracts the entangled fluctuation patterns by leveraging the advantages of two distinct decomposition methods. Nevertheless, two critical issues remain to be further explored, including (1) how to adaptively select the sub-signals to be further decomposed and (2) how to effectively capture the coupling temporal dependencies among the sub-signals. To address these issues, we propose a novel hybrid model based on an adaptive secondary decomposition (ASD) method and a robust temporal convolutional network (RTCN). Firstly, the proposed ASD method is developed to adaptively extract more sub-signals with lower complexities by combining the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), entropy-based spectral clustering (ESC), and the variational mode decomposition (VMD). Subsequently, a single RTCN is fitted to capture the temporal dependencies among the sub-signals and identify the global relationship between the sub-signals and the future wind speed. The forecasting performance is verified on four real-world wind speed datasets from different wind farms, and the experimental results demonstrate that the proposed ASD-RTCN model consistently outperforms the benchmark models in terms of forecasting accuracy and stability.