As cities worldwide pursue climate neutrality, Positive Energy Districts (PEDs)—urban areas that generate more energy than they consume—have become a focal point of sustainable urban/energy planning. Yet, assessing PED potential at the district scale remains a challenge due to data silos, fragmented data models and a lack of interoperable tools. This paper presents a modular, standards-based urban digital twin workflow that integrates detailed building-level energy simulations with district-scale energy balance analysis to evaluate PED potential. Centered on open-source/freeware tools and OGC standards, our approach leverages CityGML enriched with Energy ADE 2.0—a new and improved version of Energy ADE 1.0 currently in development—and couples SimStadt simulations with MAPED district assessments. All data and results are embedded in a 3D city database using PostgreSQL and visualised interactively through a web-based platform using OGC 3D Tiles and WFS. A real-world test in Stuttgart’s Nordbahnhofviertel district demonstrates the framework’s capability to generate actionable energy insights and confirm the area’s current shortfall in PED readiness. The methodology is now being replicated in Vienna, Rotterdam, and Wrocław, showcasing its scalability and utility for evidence-based energy planning across Europe.

This paper presents the lessons learnt from the integration of open datasets in the Netherlands for the creation of a country-wide enriched semantic 3D city model for urban building energy modelling. Although the Netherlands provides open access to building data up to the dwelling level, challenges still persist related to data fragmentation, inconsistency and incompleteness. The resulting dataset uses the CityGML with the Energy ADE data model since they offer a robust framework for integrating geospatial and non-geospatial data for energy applications. Our research highlights the need for significant preprocessing, harmonisation pipelines, and enrichment strategies to address gaps in data completeness and reliability. Finally, we identify critical missing data (e.g., renovation history, thermal zoning, and detailed HVAC specifications) and propose directions for improvement.

As urbanization accelerates, accurately simulating the heating and cooling demand of buildings becomes increasingly vital for effective energy system planning. This study proposes an urban building energy modeling framework that prioritizes data quality enhancement through pre-processing (e.g., outlier detection and repair), integrates SimStadt-based simulations, and automates post-processing for 3D database storage and visualization, validated through case studies in Rotterdam’s districts of Feijenoord and Prinsenland. The pre-processing framework targets geometric and attribute errors in municipal CityGML data by employing our proposed data repair workflow and correcting energy-critical parameters. A post-processing workflow automates the integration of simulation results into the Energy ADE-extended 3DCityDB and streamlines 3D visualization through a scalar value mapping strategy. Empirical analysis shows that the framework significantly improves the rationality and reproducibility of the heating and cooling demand results compared to those of a previous study commissioned by the municipality. This research provides a scalable technical pathway to support the evaluation of the potential of positive energy districts.