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 (Case Western Reserve University)
- 2. Xuanji Yu (Case Western Reserve University)
- 3. Raymond Wieser (Case Western Reserve University)
- 4. David Meakin (SunPower Corporation)
- 5. Avishai Shaton (SolarEdge Technologies)
- 6. Jean-Nicolas Jaubert (CSI Solar Co. Ltd.)
- 7. Robert Flottemesch (Brookfield Renewable United States)
- 8. Michael Howell (C2 Energy Capital)
- 9. Jennifer Braid (Sandia National Laboratories)
- 10. Laura Bruckman (Case Western Reserve University)
- 11. Roger French (Case Western Reserve University)
- 12. Yinghui Wu (Case Western Reserve University)
BibTeX Citation
@inproceedings{fan_sigmod23,
title = {{Spatio-Temporal Denoising Graph Autoencoders with Data Augmentation for Photovoltaic Data Imputation}},
author = {Fan, Yangxin and Yu, Xuanji and Wieser, Raymond and Meakin, David and Shaton, Avishai and Jaubert, Jean-Nicolas and Flottemesch, Robert and Howell, Michael and Braid, Jennifer and Bruckman, Laura and French, Roger and Wu, Yinghui},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3588730},
url = {https://dl.acm.org/doi/10.1145/3588730},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,913 | Inference-friendly Graph Compression for Graph Neural Networks | 2025 | VLDB | 5.093636e-05 |
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