Medium-Term Load Forecasting using TCN, LSTM, ARIMA

Overview

This repository contains the implementation and analysis of medium-term load forecasting using three different methods: Temporal Convolutional Network (TCN), Long Short-Term Memory (LSTM) neural networks, and AutoRegressive Integrated Moving Average (ARIMA). The goal is to forecast electrical energy usage based on historical data and weather conditions.

Notebooks

• Arima.ipynb: Implementation and results of the ARIMA model for load forecasting.
• LSTM.ipynb: Implementation and results of the LSTM model for load forecasting.
• TCN.ipynb: Implementation and results of the TCN model for load forecasting.

Data

• Temperature.csv: Temperature data used as a feature in load forecasting.
• building-electrical-energy-daily-use-ubcv-2019-2021.csv: Electrical energy consumption data from 2019 to 2021.

Documentation

• Presentation.pptx.pdf: A presentation summarizing the project findings and methodology.
• Report.pdf: Detailed report containing the analysis, results, and methodology of the forecasting models.

Getting Started

To run the notebooks, ensure you have Jupyter Notebook installed and the required libraries (list any major dependencies such as TensorFlow, pandas, etc.). Clone this repository and open the notebooks in Jupyter to run them.

How to Contribute

Contributions are welcome! Please fork the repository and submit pull requests with any enhancements, bug fixes, or improvements. Make sure to add a description of your changes and update the documentation as appropriate.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For any questions or comments about the project, please open an issue in the repository or contact the maintainers directly.

Acknowledgments

• Thanks to all the contributors who have helped in refining the forecasting models.
• Special thanks to data providers and academic advisors who supported this project.