This project allows you to predict energy demand effortlessly. Our model uses advanced techniques to analyze data and provide accurate forecasts for natural gas consumption. You don’t need any technical experience to get started; just follow these simple steps.
Before you begin, make sure your computer meets these requirements:
- Operating System: Windows, macOS, or Linux
- Storage: At least 500 MB of free space
- Python: Version 3.6 or higher installed on your machine. Python is essential for running our model.
- Internet Connection: Required for downloading the model and any additional data files.
- Accurate Forecasting: Uses ensemble learning techniques like Gradient Boosting, Random Forest, and Ridge Regression.
- Data Analytics: Processes multivariate time series data for reliable predictions.
- Feature Engineering: Optimizes data inputs to improve performance.
- Fourier Transforms: Analyzes data frequencies for enhanced accuracy.
- Econometric Analysis: Provides a deeper understanding of demand trends.
To get started, visit the page to download the software. Click the link below:
Once there, you will find the latest release. Follow these steps:
- Locate the Release: Look for the latest version at the top of the page.
- Download Files: Click on the asset that matches your operating system (like
.exefor Windows or.zipfor macOS/Linux). - Extract (if necessary): If you download a
.zipfile, right-click and select "Extract" to access the folder. - Run the Installer: Double-click the downloaded file to start the installation process.
- Open the Application: Locate it in your programs list and launch it.
- Load Your Data: Import your time series data using the user interface.
- Configure Settings: Adjust parameters as needed for optimal performance.
- Start Forecasting: Click the "Run" button to generate predictions.
The application will provide a clear output displaying your energy demand predictions. You will see graphs and data points that reflect your inputs.
- Predicted Values: Numbers indicating anticipated energy demand over specified intervals.
- Graphs: Visual representations of trends, showing highs and lows.
- Confidence Intervals: Understanding range for more informed decision-making.
Here are some resources that can help you understand our model better:
- Documentation: Read the full documentation for detailed information on features and settings.
- Community Support: Join discussions and ask questions on our GitHub Discussions.
- Tutorials: Visit our YouTube channel for video tutorials on using the application effectively.
If you encounter any issues or have questions, check the Issues section on GitHub. You can report bugs or seek assistance from the community.
We welcome contributions! If you’re interested in helping code or improve features, please check our Contributing Guidelines.
This project is licensed under the MIT License. For more information, please refer to the LICENSE file.