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Hierarchical gated pooling and progressive feature fusion for short-term PV power forecasting

Author

Listed:
  • Feng, Zhengkun
  • Shen, Jun
  • Zhou, Qingguo
  • Hu, Xingchen
  • Yong, Binbin

Abstract

In this paper, we propose a hierarchical gated pooling and progressive feature fusion model (HGP-PFF) for short-term photovoltaic (PV) power forecasting. HGP-PFF effectively overcomes the limitations of existing methods in multi-scale feature extraction and fusion by introducing a hierarchical gated pooling (HGP) module and a progressive feature fusion (PFF) module. This model replaces traditional convolution operations with a pooling gate mechanism for feature extraction, efficiently capturing features across different time scales. HGP-PFF also employs a PFF module to ensure the completeness and consistency of the fused feature information. The proposed HGP-PFF model is applied to three different PV power datasets collected from the Alice Springs PV power station. Compared to previous state-of-the-art (SOTA) models the proposed HGP-PFF model reduces the PV power forecasting error by more than 19.57%, 22.27% and 13.67% on these three PV power datasets.

Suggested Citation

  • Feng, Zhengkun & Shen, Jun & Zhou, Qingguo & Hu, Xingchen & Yong, Binbin, 2025. "Hierarchical gated pooling and progressive feature fusion for short-term PV power forecasting," Renewable Energy, Elsevier, vol. 247(C).
    • Handle: RePEc:eee:renene:v:247:y:2025:i:c:s0960148125005919
    DOI: 10.1016/j.renene.2025.122929
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