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Dynamic Spatio-Temporal Graph-Enhanced KANformer for high-fidelity ultra-short-term wind power forecasting

Author

Listed:
  • Shi, Zhihan
  • Zhang, Guangming
  • Lu, Chao
  • Zhou, Xiaoxiong
  • Lv, Xiaodong

Abstract

Ultra-short-term wind power forecasting (WPF) is crucial for power system stability, yet challenged by wind’s inherent volatility and complex spatio-temporal dependencies. This paper introduces a novel Dynamic Spatio-Temporal Graph-Enhanced KANformer (STGAT-KANformer) for high-fidelity WPF at 15 min, 1-hour, and 2-hour horizons. The STGAT-KANformer first utilizes a dynamic spatio-temporal Graph Attention Network (GAT) to capture evolving interdependencies among meteorological and historical data, yielding enriched feature representations. These are then processed by a KANformer backbone, which synergizes Transformer’s sequence modeling with Kolmogorov–Arnold Network (KAN) layers featuring learnable spline-based activations for superior function approximation and potential interpretability. This hierarchical design efficiently learns intricate nonlinearities and fine-grained temporal patterns in high-frequency wind power series.

Suggested Citation

  • Shi, Zhihan & Zhang, Guangming & Lu, Chao & Zhou, Xiaoxiong & Lv, Xiaodong, 2025. "Dynamic Spatio-Temporal Graph-Enhanced KANformer for high-fidelity ultra-short-term wind power forecasting," Energy, Elsevier, vol. 337(C).
    • Handle: RePEc:eee:energy:v:337:y:2025:i:c:s036054422504232x
    DOI: 10.1016/j.energy.2025.138590
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