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Short-term multi-site solar irradiance prediction with dynamic-graph-convolution-based spatial-temporal correlation capturing

Author

Listed:
  • Zang, Haixiang
  • Li, Wenan
  • Cheng, Lilin
  • Liu, Jingxuan
  • Wei, Zhinong
  • Sun, Guoqiang

Abstract

To optimize the utilization of photovoltaic power sources, high-precision short-term solar irradiance predictions are critically needed. This study presents a novel short-term solar irradiance prediction method utilizing dynamic graph convolution, which captures spatial-temporal correlations across multiple sites. The method takes historical solar irradiance and meteorological data as input. Initially, an adaptive feature selection approach based on squeeze-and-excitation networks (SENets) is introduced to assign appropriate weights to various meteorological features. Then, a self-attention mechanism is integrated into the adjacency matrix construction method for graph convolutional networks (GCNs), allowing for the extraction of spatial features. Finally, the spatial-temporal features extracted through a bi-directional long short-term memory (BiLSTM) network are used for short-term prediction. Experimental results demonstrate that the proposed hybrid model exhibits superior generalization capability and greater prediction accuracy compared to the benchmark methods, significantly improving the accuracy of short-term global horizontal irradiance (GHI) prediction.

Suggested Citation

  • Zang, Haixiang & Li, Wenan & Cheng, Lilin & Liu, Jingxuan & Wei, Zhinong & Sun, Guoqiang, 2025. "Short-term multi-site solar irradiance prediction with dynamic-graph-convolution-based spatial-temporal correlation capturing," Renewable Energy, Elsevier, vol. 246(C).
    • Handle: RePEc:eee:renene:v:246:y:2025:i:c:s096014812500607x
    DOI: 10.1016/j.renene.2025.122945
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