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
| # Quickstart: FELA Grid Renewable | |
| A minimal example that loads the forecaster, runs one weather input window, and prints the | |
| P10 to P90 power band for the first forecast hour. It runs on CPU; no GPU is required. | |
| ## Install | |
| Pinned versions, runs from a clean virtual environment: | |
| ``` | |
| pip install -r requirements.txt | |
| ``` | |
| ## Get the weights | |
| The safetensors weights ship in the model repo (`solar.safetensors`, `wind.safetensors`). | |
| `--weights` defaults to the track's safetensors file, or point it at a local path. | |
| ## Run | |
| Solar (default): | |
| ``` | |
| python run.py --track solar --weights /path/to/solar.safetensors | |
| ``` | |
| Wind: | |
| ``` | |
| python run.py --track wind --weights /path/to/wind.safetensors | |
| ``` | |
| The script loads with the bundled `modeling.load_model` (a few line load) and preprocesses | |
| the window with `modeling.preprocess_nwp`, which standardizes it and validates the shape | |
| (it fails clearly on the wrong shape). | |
| ## What you should see | |
| The script prints the output shape `(1, 99)` and, for the center forecast hour, | |
| the P10 (low), P50 (median), and P90 (high) power levels as a fraction of site capacity, | |
| plus the width of the P10 to P90 uncertainty band. Input shapes are solar `(1, 6, 20)`, | |
| wind `(1, 12, 15)`. | |
| The example uses a random input window so it runs without a data download. Replace it with | |
| a real NWP window (the same shape) to get a real forecast. | |