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README.md

@@ -1,3 +1,28 @@
----
-license: mit
----
+---
+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)
+```