Data-Driven Energy Simulations To Evaluate Positive Energy District Potential In Rotterdam

Abstract. 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.