Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月28日

查看全文
  • Zhiqiang Jiang,
  • Changfu You,
  • Dong Ma,
  • Sida Xu,
  • Haolong Xiang

摘要

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.