A noise-robust framework for multi-step wind power forecasting via self-supervised joint optimization

Short-term wind power forecasting remains a critical yet challenging task due to the inherent volatility and intermittency of wind. While deep learning has shown promise, it is still prone to noise and cumulative errors in multi-step prediction. This paper presents Time Self-Supervised Contrastive Learning (TimeSCL), a novel framework that integrates contrastive learning with self-supervised joint optimization to enhance robustness and accuracy. TimeSCL introduces an adaptive noise injection mechanism to generate clean—noisy sample pairs, enabling the model to learn noise-invariant representations. A dual forward propagation strategy contrasts these representations before and after denoising, improving temporal modeling. To further mitigate overfitting and error accumulation, TimeSCL incorporates dropout and a time-to-frequency domain loss function, jointly capturing temporal and spectral features. Additionally, a progressive contrastive loss weighting (PCLW) strategy is employed during training to dynamically balance the contributions of different loss components, ensuring stable and effective optimization. Extensive experiments conducted on diverse public benchmarks and real-world wind turbine datasets show that TimeSCL consistently outperforms state-of-the-art baselines, achieving up to a 13.6% reduction in mean squared error and a 5.4% reduction in mean absolute error. These results demonstrate the framework’s effectiveness and practicality for robust wind power forecasting in noisy and complex environments.