paper
2023
Applications & impacts
Post-processing & calibration New model or method
RNN / LSTM / ConvLSTMClassical ML (trees, GP, linear, kernels)Reinforcement learning6 h–3 days Journal
2023-01-01
008
Jan 2023
Liang Zeng, Xin Lan, Shanshan Wang
Journal of Renewable and Sustainable Energy
Hybrid GWO-WNN, VMD-LSTM and Q-learning model combines NWP forecasts with wind power series to improve short-term wind power prediction accuracy.
Short-term wind power prediction based on the combination of numerical weather forecast and time series
In one sentence. Hybrid GWO-WNN, VMD-LSTM and Q-learning model combines NWP forecasts with wind power series to improve short-term wind power prediction accuracy.
Labels
- Topic
- Applications & impacts, Post-processing & calibration
- Type
- New model or method
- Method
- RNN / LSTM / ConvLSTM, Classical ML (trees, GP, linear, kernels), Reinforcement learning
- Time scale
- 6 h–3 days
- Domain
- Local / station
- Data
- NWP analysis or forecasts (IFS, GFS, ...), Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1063/5.0123759 OpenAlex:W4313682926
- Added
- 2026-10-22
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