paper
2022
Local & station forecasting
New model or method
Transformer / ViTRNN / LSTM / ConvLSTM6 h–3 daysNanjing University of Information Science & Technology (NUIST) Journal
2022-02-02
018
Feb 2022
Liu Zhifeng, Ding Feng, J. Y. Lu et al.
Proceedings of the Bulgarian Academy of Sciences
Nanjing University of Information Science & Technology (NUIST)
BLSTM-attention and BLSTM-Transformer models predict wind speed at seven Qingdao stations, cutting RMSE by about 44% versus ECMWF forecasts.
Comparison of BLSTM-Attention and BLSTM-Transformer Models for Wind Speed Prediction
In one sentence. BLSTM-attention and BLSTM-Transformer models predict wind speed at seven Qingdao stations, cutting RMSE by about 44% versus ECMWF forecasts.
Labels
- Topic
- Local & station forecasting
- Type
- New model or method
- Method
- Transformer / ViT, RNN / LSTM / ConvLSTM
- Time scale
- 6 h–3 days
- Domain
- Local / station
- Data
- Station / in-situ, NWP analysis or forecasts (IFS, GFS, ...)
- Status
- Research
- Identifiers
- DOI:10.7546/crabs.2022.01.10 OpenAlex:W4210832701
- Added
- 2026-10-22
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