paper
2022
Applications & impacts
New model or method
Other6 h–3 days Journal
2022-11-24
012
Nov 2022
Hong Liu, Zijun Zhang
IEEE Transactions on Power Systems
Privacy-preserving bi-party framework learns local and global latent features from multi-site NWP inputs for renewable power forecasts, with at least 3% average gains over baselines.
A Bi-Party Engaged Modeling Framework for Renewable Power Predictions With Privacy-Preserving
In one sentence. Privacy-preserving bi-party framework learns local and global latent features from multi-site NWP inputs for renewable power forecasts, with at least 3% average gains over baselines.
Labels
- Topic
- Applications & impacts
- Type
- New model or method
- Method
- Other
- Time scale
- 6 h–3 days
- Domain
- Regional
- Data
- NWP analysis or forecasts (IFS, GFS, ...)
- Status
- Research
- Identifiers
- DOI:10.1109/tpwrs.2022.3224006 OpenAlex:W4312400898
- Added
- 2026-10-22
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