Development of a Scalable Modular Backend for an Urban Energy Co-simulation Platform

Urban energy systems play a pivotal role in addressing global sustainability challenges, significantly impacting energy consumption and greenhouse gas emissions. This thesis introduces the development of a modular backend system, utilizing a microservice architecture, to automate the creation of urban energy scenarios. The research focuses on resolving critical issues of data accessibility, heterogeneity, and scalability in urban energy system modelling. The methodology is structured into distinct phases. A scalable microservice-based architecture was designed to manage and streamline geospatial data processing and enrichment. Advanced data validation techniques and spatial interpolation methods were employed to address gaps in building attributes. The system generates standardized GeoJSON outputs, which are stored in a database and serve as inputs for the co-simulation of multi-energy systems, ensuring seamless integration and practical application. The system’s effectiveness was demonstrated using datasets from Turin, Italy. The backend consistently produced high-quality, simulation-ready datasets by integrating diverse data sources such as OpenStreetMap, census records, DTM (Digital Terrain Model) and DSM (Digital Surface Model) data. The modular architecture ensures scalability, fault tolerance, and adaptability, enabling stakeholders—including policymakers, urban planners, and researchers—to perform precise and efficient urban energy analyses. This research advances the field of urban energy modelling by addressing persistent challenges in automation, standardization, and accessibility of data preparation. While the system establishes a robust foundation for energy simulations, future work could focus on integrating real-time data and expanding its applicability to additional urban domains, further supporting sustainable urban development.