Urban Morphology for Energy Resilience (UMER)
UM4ENERGY is a research project focused on understanding the relationship between urban morphology and energy consumption. The project aims to quantify the impact of urban form on energy use using advanced machine learning techniques, specifically Graph Neural Networks (GNNs) and Explainable Artificial Intelligence (XAI). This initiative seeks to provide actionable insights to urban planners and policymakers to promote sustainable urban development.
- UMER (Urban Morphology and Energy Resilience): Focuses on how urban form influences energy resilience in cities.
- UMEC (Urban Morphology Energy Consumption): Quantifies the energy consumption patterns influenced by urban morphology.
- UMEG (Urban Morphology Energy Generation): Explores the potential for energy generation within urban environments based on urban form.
Title: Quantifying the Impact of Urban Form on Energy Consumption Using Graph Neural Networks and Interpretable Artificial Intelligence
This study investigates the impact of urban morphology on energy consumption using advanced machine learning techniques. By leveraging Graph Neural Networks (GNNs) and Explainable Artificial Intelligence (XAI), we aim to provide a comprehensive understanding of how urban form influences energy use. The findings offer valuable insights for urban planners and policymakers to design more sustainable and energy-efficient cities.
- Quantify the Impact of Urban Morphology on Energy Consumption: Use GNNs to model the relationship between urban form and energy use.
- Provide Actionable Insights: Apply XAI techniques to interpret the results and identify key factors influencing energy consumption.
- Support Sustainable Urban Planning: Offer data-driven recommendations for urban planners to optimize urban form for energy efficiency.
- Data Collection:
- Urban Morphology Data: Building height, area, orientation, compactness, and green spaces.
- Energy Consumption Data: Building energy use, transportation energy use, and overall city energy consumption.
- Graph Construction:
- Represent urban morphology as a graph with nodes (buildings or areas) and edges (spatial relationships).
- Model Training:
- Train GNN models to predict energy consumption based on urban morphology characteristics.
- Interpretation:
- Use XAI techniques to interpret the model results and identify key urban form factors.
- Data Collection: Urban morphology and energy consumption data from Wuhan.
- Model Training: GNN model trained to predict energy consumption.
- Interpretation: GNNExplainer used to identify key factors influencing energy consumption.
- Key Findings:
- Building Form: Building height, compactness, and green space significantly impact energy consumption.
- Neighborhood Street Accessibility: Street layout and traffic flow density affect transportation energy use.
- Parcel Land Use: Land use mix and green space distribution impact both building and transportation energy consumption.
- Interpretation: XAI techniques provide insights into the most influential urban form factors.
- Summary: Highlights the key findings and the effectiveness of using GNN and XAI to quantify the impact of urban morphology on energy consumption.
- Implications: Discusses the implications for urban planning and policy-making, emphasizing the importance of sustainable urban design.
- Future Work: Suggests areas for further research, such as exploring the impact of different urban planning strategies on energy consumption.
- Python 3.7 or higher
- Required libraries: TensorFlow, PyTorch, NetworkX, GNNExplainer
pip install -r requirements.txt
### Usage
```python
python main.py --data_path "data/urban_morphology.csv" --model_type "GNN"Contributions are welcome! Please read the Contributing Guide for details.
This project is licensed under the MIT License - see the LICENSE file for details.
For any questions or inquiries, please contact us at [email protected].
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### Key Enhancements:
1. Added a clear project overview and context.
2. Included a detailed abstract and research objectives.
3. Provided a structured methodology section.
4. Added a case study section with specific examples.
5. Included a "Getting Started" section with prerequisites and usage instructions.
6. Added contributing guidelines and contact information.
7. Included licensing and acknowledgment sections.
This README.MD file now provides a comprehensive overview of the project, making it easier for users and contributors to understand and engage with the research.