# AI and Kernel Influences: A Study on Improving Energy Forecasting Accuracy

Welcome to the repository for "AI and Kernel Influences: A Study on Improving Energy Forecasting Accuracy". This project explores advanced machine learning methodologies for enhancing short-term energy forecasting accuracy, focusing on the development and evaluation of an LSTM-based HyperNetwork model that incorporates Kernel analysis for improved prediction precision.

## Project Overview
The goal of this project is to refine the accuracy of energy forecasting models by integrating advanced Kernel methods with traditional LSTM networks. This approach is tested against standard models like MLPs, RNNs, and Transformers to provide a comprehensive comparative analysis.

## Key Features
- **Model Customization**: Users can customize any model within the project by modifying the `training.py` file. Model architectures are detailed in the `model.py` file for ease of understanding and modification.
- **Comprehensive Model Repository**: All model architectures are systematically organized within the `models` directory. This repository structure supports easy navigation and usage of various predictive models.
- **Detailed Results and Analysis**: Performance metrics and analytical results are thoroughly documented and stored in the `Testing` directory. This ensures that users can easily assess and compare the effectiveness of different models.

## Getting Started
To begin using this project, follow these steps:
1. **Clone the Repository**:
2. **Set Up Your Environment**:
- Ensure that Python 3.8 or newer is installed.
- Install required dependencies:
  ```
  pip install -r requirements
  ```

## Usage
To run a model and evaluate its performance, navigate to the repository's root directory and execute:
This script will train the model and save the forecasting results and metrics in the `Testing` directory.

## Contributing
Contributions to improve the models or extend the repository are welcome. Please follow the standard pull request process:
- Fork the repository.
- Create a new branch for your feature (`git checkout -b feature/AmazingFeature`).
- Commit your changes (`git commit -m 'Add some AmazingFeature'`).
- Push to the branch (`git push origin feature/AmazingFeature`).
- Open a pull request.

## Citation
If you use the models or the findings from this repository in your research, please cite:
