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House-energy-saving-dummy-model

Overview

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.

Features

  • 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.

Usage

  1. Adjust the sliders to simulate house area, renovation cost, and the expected savings from various upgrades.
  2. 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.
  3. Use the sliders to explore different scenarios and their financial implications.

Technologies Used

  • Streamlit: For the interactive web interface.
  • Matplotlib: For plotting graphs and visualizing energy savings.
  • Pandas and NumPy: For data manipulation and calculations.

Installation

  1. Clone the repository: 
    git clone https://github.com/yourusername/House-energy-saving-dummy-model.git
  2. Install the required dependencies: 
    pip install -r requirements.txt
  3. Run the Streamlit app: 
    streamlit run app.py

Requirements

To run this project, ensure that your requirements.txt file includes:

This setup works best with Python 3.10.

Limitations

  • 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.

Future Improvements

  • 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.

License

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