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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)
```
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