 Hierarchical gated pooling and progressive feature fusion for short-term PV power forecastingZhengkun Feng, Jun Shen, Qingguo Zhou, Xingchen Hu and Binbin Yong Renewable Energy, 2025, vol. 247, issue C 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. Keywords: Hierarchical gated pooling; Progressive feature fusion; Pooling gate mechanism; Short-term PV power forecasting (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:247:y:2025:i:c:s0960148125005919 DOI: 10.1016/j.renene.2025.122929 Access Statistics for this article Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides More articles in Renewable Energy from Elsevier |
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