metadata
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
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)