☀️ Photovoltaic Power Forecasting using Hybrid Model

Forecast solar or photovoltaic power output using an end-to-end, reproducible machine learning pipeline combining traditional regression, deep learning (LSTM), and hybrid modeling approaches.

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🚀 Features

• Flexible data preprocessing and feature engineering
• Supports Random Forest, LSTM, and hybrid ensemble modeling
• Detailed evaluation: MAE, RMSE, (Adjusted) R², and per-epoch error tracking
• Modular source code for clarity and maintainability
• Jupyter/Colab notebook for step-by-step demonstration
• Well-documented datasets and sample files for quick starts

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📂 Project Structure

Photovoltaic-Power-Forecasting-using-Hybrid-Model/
├── data/           # Organized datasets (train/test/raw) + README
├── notebook/       # Jupyter/Colab notebooks (main workflow)
├── requirements/   # Python dependencies (requirements.txt, README)
├── results/        # Model metrics (results.json), key error plots, README
├── samples/        # Example input/output files for quick testing, README
├── src/            # Core code: preprocessing, feature engineering, training, utils
├── .gitignore
├── LICENSE
├── README.md

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🛠️ Requirements

Install all dependencies in a fresh environment:

pip install -r requirements/requirements.txt

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📊 Results

See results/ for:

• Summary table (results.json): adjusted R² for Random Forest, LSTM, hybrid models
• Convergence/error plots: mae_vs_epoch.png, rmse_vs_epoch.png, r2_vs_epoch.png
• Example result:

  Model	Adjusted R²
  RandomForest	0.99976
  LSTM	0.99869
  HybridModel	0.99970

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📁 Data

See data/README.md for folder and file organization, sample columns, and preparation notes.
(Note: Large/full datasets are not tracked in git. Use provided samples for testing.)

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📌 Folder Guide

• data/ — All datasets (train, test, raw, combined)
• notebook/ — Demo notebook (with Colab link)
• requirements/ — Dependency file(s)
• results/ — Metrics, plots, and performance summaries
• samples/ — Example input/output files
• src/ — Modular Python code for the ML pipeline

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📝 License

Distributed under the MIT License.

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👤 Author

Kamal-Shirupa
Contributions and feedback are welcome!
Create an issue or a pull request for suggestions, improvements, or questions.

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