Computationally scalable geospatial network and routing analysis through multi-level spatial clustering

Area of Science:

• Geographic Information Systems (GIS)
• Network Analysis
• Thermal Energy Systems

Background:

• Thermal grids are crucial for decarbonizing thermal energy supply.
• Existing network analysis methods struggle with large-scale geospatial datasets.
• Computational scaling is a significant challenge for current approaches.

Purpose of the Study:

• To present a system for geospatial modeling of thermal networks.
• To enable routing and flow calculations within existing road networks.
• To overcome computational limitations of previous thermal network analyses.

Main Methods:

• Application of multi-level spatial clustering for parallelization.
• Development of algorithms and data processing pipelines for network routing.
• Implementation of cluster-level caching for rapid model evaluation.

Main Results:

• Successfully modeled thermal networks with routing through road infrastructure.
• Enabled calculation of network flows at large geographic scales.
• Demonstrated efficient handling of large datasets, overcoming previous limitations.

Conclusions:

• The developed system offers a scalable solution for geospatial thermal network modeling.
• This approach supports more accurate and efficient analysis of thermal grids.
• Facilitates improved planning and operation for thermal energy decarbonization efforts.