paper
2020
Applications & impacts
Post-processing & calibration New model or method
RNN / LSTM / ConvLSTM6 h–3 days Journal
2020-12-24
018
Dec 2020
Xiaosheng Peng, Hongyu Wang, Jianxun Lang et al.
Energy
EALSTM-QR produces interval wind-power forecasts from numerical weather prediction inputs using deep learning; the title suggests an energy-sector application (no abstract).
EALSTM-QR: Interval wind-power prediction model based on numerical weather prediction and deep learning
In one sentence. EALSTM-QR produces interval wind-power forecasts from numerical weather prediction inputs using deep learning; the title suggests an energy-sector application (no abstract).
Labels
- Topic
- Applications & impacts, Post-processing & calibration
- Type
- New model or method
- Method
- RNN / LSTM / ConvLSTM
- Time scale
- 6 h–3 days
- Domain
- Regional
- Data
- NWP analysis or forecasts (IFS, GFS, ...)
- Status
- Research
- Identifiers
- DOI:10.1016/j.energy.2020.119692 OpenAlex:W3117620741
- Added
- 2026-10-22
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