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Missing data-aware robust electrical load forecasting based on hierarchical downsampling-upsampling spatiotemporal graph network
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DOI：10.1016/j.apenergy.2025.127209.png)
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Abstract
En 中文
• Propose HDU-STGNN, an end-to-end load forecaster that is robust to missing data. • Introduce mask-aware temporal downsampling and hierarchical spatial coarsen–refine. • Achieve up to 17.4 % error reduction under 90 % missing rates on real load data.
Journal
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11
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2.6W
Citations:
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