Cloud–edge collaborative intelligence for wind power forecasting: large–small model synergy with LightGBM and CNN–BiLSTM

Abstract

Accurate short-term wind power forecasting (STWPF) is critical to maintaining grid stability and improving renewable integration. Physical, statistical, and deep-learning methods are widely used and have produced promising results. However, the above methods often struggle to balance prediction accuracy with computational efficiency and to capture spatiotemporal dependencies while keeping hyperparameter tuning manageable. To address these limitations, we propose a novel cloud–edge collaborative intelligence framework which enables synergy between large and small models for STWPF. The framework consists of two components: a LightGBM module for efficient feature selection, and a cloud module with a temporal 1D Convolutional Neural Network (1D-CNN) for local temporal pattern extraction and cross-channel interactions, followed by a Bidirectional Long Short-Term Memory (BiLSTM) for bidirectional temporal modeling. Moreover, we use a resource aware tuning strategy that speeds up tuning without loss of accuracy. Through extensive experiments on real-world datasets, our method outperforms state-of-the-art baselines, highlighting the practical value of our framework.