SMWPF: a cloud-edge collaborative network enabled by large-small model collaboration for wind speed prediction

Abstract

Accurate wind speed forecasting is increasingly important for supporting reliable wind power grid integration and enhancing early warning of extreme wind events. Existing spatiotemporal models can fuse multiple meteorological variables and perform well in medium-resolution wind field forecasts. However, when handling high-resolution data, they often produce overly smooth wind fields, miss local details, and exhibit large errors. To address these problems, we propose SMWPF, an edge-cloud collaborative framework for high-resolution wind field prediction. A lightweight edge-side predictor first generates low-latency forecasts. Its latent representations are refined by soft vector quantization using a two-layer multilayer perceptron (MLP) and an extended codebook, which helps alleviate over-smoothing. An extreme wind speed factor is fused with elevation information to guide the attention module toward regions historically susceptible to strong winds. The framework then estimates the confidence of each edge forecast. Low-confidence samples are sent to a Qwen-based cloud predictor, which maps the original normalized historical meteorological tensor to the future wind field for re-prediction. Extensive experimental results on both private and public datasets indicate that SMWPF reduces root mean square error (RMSE) by 5% on average across datasets, outperforming state-of-the-art methods.