Spatio-Temporal Denoising Graph Autoencoders with Data Augmentation for Photovoltaic Data Imputation
Summary: Introduces STD-GAE, a spatio-temporal denoising graph autoencoder for PV data imputation, using domain-knowledge augmentation. Domain-aware augmentation yields robust fleet imputation across missing patterns and seasons; 43.14% accuracy gain vs SOTA. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Yangxin Fan
- 2. Xuanji Yu
- 3. Raymond Wieser
- 4. David Meakin
- 5. Avishai Shaton
- 6. Jean-Nicolas Jaubert
- 7. Robert Flottemesch
- 8. Michael Howell
- 9. Jennifer Braid
- 10. Laura Bruckman
- 11. Roger French
- 12. Yinghui Wu
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,663 | Inference-friendly Graph Compression for Graph Neural Networks | 2025 | VLDB | 4.1945683e-05 |
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