Enhancing urban energy modeling: A case study of data acquisition, enrichment, and evaluation in Berlin

Abstract

Urban Building Energy Modeling (UBEM) has become a critical tool for developing local heating and cooling plans, as required by the European Union. Despite growing interest, the reproducibility and reliability of UBEM studies remain limited due to data scarcity and workflow complexity. This paper presents a comprehensive framework to evaluate the data pipeline in UBEM, with a particular focus on data acquisition, enrichment, simulation, calibration, and information application. The approach applies three distinct UBEM workflows (CityEnergyAnalyst, DistrictGenerator, and SimStadt) to the Mierendorffinsel district in Berlin, Germany. We compare the based on quantitative performance metrics and qualitative framework criteria. The results highlight the influence of data sources, archetype definitions, and geometric preprocessing on simulation outcomes. The CEA models consider between 365 and 646 buildings, depending on the scenario. The study provides guidelines for practitioners to enhance model transparency, reproducibility, and accuracy in urban energy modeling. Although official data provide more accurate building functions, the geometries need extensive preprocessing. The residential archetypes are far more refined, e.g., in the status of renovation, which reflects the amount of residential buildings compared to nonresidential buildings. We show that evaluation threshold criteria for the district level are scarce and evaluate multiple metrics. Results depend on the selected evaluation method, but observed differences are generally higher in case of nonresidential buildings, with differences of more than 300 kWh/m${}^2$ for several nonresidential building types. The heated area considered differs up to a factor of 1.9, because of different buildings, metadata, and calculation approaches.