| --- |
| license: mit |
| tags: |
| - sklearn |
| - solar-energy |
| - time-series |
| - regression |
| --- |
| |
| # Solar Power Forecast Model |
|
|
| RandomForestRegressor trained to predict plant-level DC power output |
| 15 minutes ahead using weather sensor data and lag features. |
|
|
| **Dataset**: Kaggle Solar Power Generation Data (Plant 1, 34 days, 15-min intervals) |
| **Features**: irradiation, ambient temp, module temp, hour, day_of_year, month, lag_1, lag_4, rolling_mean_4 |
| **R² (daytime)**: 0.9905 |
| **R² (full dataset)**: 0.9323 |
|
|
| ## Usage |
|
|
| ```python |
| import joblib |
| from huggingface_hub import hf_hub_download |
| |
| path = hf_hub_download(repo_id="nakedved/genai-capstone", filename="solar_forecast_model.pkl") |
| model = joblib.load(path) |
| ``` |
|
|
