This repository is dedicated to forecasting solar irradiance using Artificial Neural Networks (ANNs) developed from scratch. It includes implementations of both a simple linear regression model for baseline comparison and a more complex ANN for detailed analysis.
- Data:
- Contains the datasets used for training and testing the models, ensuring robust model evaluations.
- Documentation:
- Provides comprehensive documentation on the methodology, model architecture, and results.
- Models:
LinearRegression.py: Implements a straightforward linear regression model as a baseline for forecasting performance.NeuralNetwork.py: Develops a neural network model tailored for the nuances of solar irradiance data.
main.py: The main script that ties together data handling, model training, and forecasting tasks.
To run the forecasting models, ensure you have Python and necessary libraries (e.g., NumPy, Pandas, TensorFlow) installed. Clone the repository, navigate to the root directory, and execute main.py to start the experiments.
Contributions are welcome! If you have enhancements, additional models, or improvements, please fork the repository and submit a pull request with your proposed changes.
This project is licensed under the MIT License - see the LICENSE.md file for details.
For queries or further collaboration, please open an issue in this repository or contact the project maintainers directly.
We extend our heartfelt thanks to all contributors, data providers, advisors, and everyone who has supported this project. Your insights and contributions have been crucial in advancing our understanding of solar irradiance forecasting.