The House-energy-saving-dummy-model is a simple, dummy model designed to simulate basic energy savings for individual houses based on renovation upgrades. It calculates break-even points, cumulative energy savings, and return on investment (ROI) for renovations like insulation, window improvements, and HVAC upgrades.
This model is only for testing purposes and is not a full-fledged energy simulation tool. It focuses on individual houses rather than district-wide or large-scale energy analysis.
- Interactive Sliders: Allows you to adjust house parameters like area, renovation cost, and savings from insulation, window, and HVAC upgrades.
- Cumulative Savings Graph: Visualizes cumulative energy savings over a 20-year period.
- Break-even Point Calculation: Identifies the year when the savings from energy efficiency measures exceed renovation costs.
- Return on Investment (ROI): Shows the ROI for the renovation project.
- Adjust the sliders to simulate house area, renovation cost, and the expected savings from various upgrades.
- View the graph, which shows:
- Cumulative savings over time.
- Renovation costs as a reference line.
- The break-even point where savings equal the renovation cost.
- Use the sliders to explore different scenarios and their financial implications.
- Streamlit: For the interactive web interface.
- Matplotlib: For plotting graphs and visualizing energy savings.
- Pandas and NumPy: For data manipulation and calculations.
- Clone the repository:
git clone https://github.com/yourusername/House-energy-saving-dummy-model.git
- Install the required dependencies:
pip install -r requirements.txt
- Run the Streamlit app:
streamlit run app.py
To run this project, ensure that your requirements.txt file includes:
This setup works best with Python 3.10.
- This model is designed for individual houses only and does not scale to district-level or large-scale energy assessments.
- It is a dummy model meant for testing and demonstration purposes. The results are simplified and do not reflect actual energy simulations or comprehensive life cycle assessments (LCA).
- The model does not include advanced machine learning or data-driven insights but serves as a conceptual demo of energy savings.
- Extend the model to handle multiple buildings and district-level energy analysis.
- Add machine learning models to predict savings based on historical data.
- Integrate environmental impact metrics like carbon footprint reduction.
This project is licensed under the MIT License. See the LICENSE file for details.