DeepWind1.0-33M

DeepWind1.0-33M is the ~33M-parameter small checkpoint of DeepWind, a decoder-only Transformer foundation model for zero-shot probabilistic wind-power forecasting, introduced in

H. Wang et al., "DeepWind: A foundation model for zero-shot wind power forecasting," Energy, vol. 360, 141793, 2026. https://doi.org/10.1016/j.energy.2026.141793

It is the smallest of the DeepWind family — light enough to run on a laptop GPU — yet it still issues a 21-quantile forecast directly from a historical power context and, when available, meteorological covariates and site coordinates.

Model description

• Architecture: decoder-only Transformer with time-aware patching, decoupled time/variate attention (RoPE + xPOS), sparse top-2 mixture-of-experts layers, and a direct multi-quantile prediction head.
• Scale: ~33M trainable parameters (6 layers, hidden 384, 6 heads, SwiGLU width 1024, 4 experts).
• Output: 21 quantiles (0.01–0.99) per site and horizon.
• Loss: trained with a power-only channel loss (channel_loss_weights = [1, 0, 0, 0, 0, 0]), i.e. the objective concentrates gradient on the power channel, which is the channel zero-shot evaluation scores.

Usage

# install the model code first:
#   pip install git+https://github.com/Hexian-2001/DeepWind.git

from src.models.deepwind import DeepWindModel

model = DeepWindModel.from_pretrained("Hexian-2001/DeepWind1.0-33M")

Full forecasting (data preparation, expand-collapse decoding, and post-processing) is provided in the DeepWind repository (src/inference/, infer.py):

python infer.py \
  model=deepwind_small \
  inference.checkpoint_path=Hexian-2001/DeepWind1.0-33M \
  data.npy_path=/path/to/site.npy \
  data.metadata_path=/path/to/metadata.csv

Architecture

Setting	Value
hidden size (d_model)	384
layers / heads	6 / 6
FFN width (d_ff)	1024 (SwiGLU)
experts / top-k	4 / 2
patch size / stride	16 / 16
max context	8192
quantiles	21 (0.01–0.99)
normalization	RMSNorm + arcsinh
positional encoding	RoPE + xPOS
trainable parameters	~33 M

Training

Trained with AdamW (β₁=0.9, β₂=0.95), peak learning rate 1e-4, 3,000-step linear warmup, cosine decay, weight decay 0.01, gradient clipping at 1.0, BF16, and a global batch size of 256 for 100,000 steps.

Evaluation

Zero-shot results on WindBench (8 datasets), as reported in the paper. Each horizon is the arithmetic mean across the eight datasets:

Horizon	nCRPS ↓	nMAE ↓
1 h	0.0425	0.0579
2 h	0.0648	0.0804
4 h	0.0925	0.1107
6 h	0.1075	0.1319
8 h	0.1175	0.1478
12 h	0.1275	0.1741
mean	0.0921	0.1171

Limitations

This is a research model. Prediction intervals should be validated and recalibrated on the deployment site; performance may degrade under unseen turbine controls, curtailment policies, sensor faults, or covariate conventions. The proprietary Shanxi Wind source cannot be redistributed.

Citation

@article{wang2026deepwind,
  title     = {DeepWind: A foundation model for zero-shot wind power forecasting},
  author    = {Wang, Hexian and Zhou, Tongming and Jia, Chengzhen and Liu, Yushan and Wang, Lingmei},
  journal   = {Energy},
  volume    = {360},
  pages     = {141793},
  year      = {2026},
  doi       = {10.1016/j.energy.2026.141793}
}

License

Apache-2.0. See the DeepWind repository for the code license and CITATION.cff.