Time Series Forecasting
Transformers
Safetensors
fela_grid_renewable
feature-extraction
fela
fourier-neural-operator
fno
cpu
on-device
energy-forecasting
solar-power
wind-power
probabilistic-forecasting
quantile-regression
custom_code
Instructions to use lowdown-labs/fela-power-grid with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lowdown-labs/fela-power-grid with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lowdown-labs/fela-power-grid", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| import argparse | |
| import os | |
| import sys | |
| import torch | |
| sys.path.insert(0, os.path.dirname(__file__)) | |
| from modeling import load_model | |
| SHAPES = {"solar": (1, 6, 20), "wind": (1, 12, 15)} | |
| VERIFICATION = {"solar": 1e-06, "wind": 0.446117} | |
| TOL = 0.001 | |
| def fixed_input(track): | |
| torch.manual_seed(0) | |
| return torch.randn(*SHAPES[track]) | |
| def main(): | |
| ap = argparse.ArgumentParser() | |
| ap.add_argument("--track", choices=["solar", "wind"], required=True) | |
| ap.add_argument("--weights", default=".") | |
| args = ap.parse_args() | |
| model = load_model(args.weights, track=args.track) | |
| x = fixed_input(args.track) | |
| with torch.no_grad(): | |
| out = model(x) | |
| if out.dim() != 2 or out.shape[0] != 1 or out.shape[-1] != 99: | |
| print(f"Fail: unexpected output shape {tuple(out.shape)}, expected (1, 99)") | |
| sys.exit(1) | |
| p50 = out[0, 49].item() | |
| print(f"Captured first-hour P50 for {args.track}: {p50:.6f}") | |
| ref = VERIFICATION[args.track] | |
| if abs(p50 - ref) > TOL: | |
| print( | |
| f"Fail: P50 {p50:.6f} differs from verification {ref:.6f} by more than {TOL}" | |
| ) | |
| sys.exit(1) | |
| print(f"Verification check OK (P50 within {TOL} of {ref:.6f})") | |
| sys.exit(0) | |
| if __name__ == "__main__": | |
| main() | |