Abstract
Urban Building Energy Modelling (UBEM) requires adequate geometrical information to represent buildings in a 3D digital form. However, open data models usually lack essential information, such as building geometries, due to a lower granularity in available data. For heating demand simulations, this scarcity impacts the energy predictions and, thereby, questioning existing simulation workflows. In this paper, the authors present an open-source CityGML LoD Transformation (CityLDT) tool for upscaling or downscaling geometries of 3D spatial CityGML building models. With the current support of LoD0–2, this paper presents the adapted methodology and developed algorithms for transformations. Using the presented tool, the authors transform open CityGML datasets and conduct heating demand simulations in Modelica to validate the geometric processing of transformed building models.
Highlights
In 2018, about 55% of the world’s population resided in urban areas
Using keywords “Urban Building Energy Modelling (UBEM)”, “Geographical Information System (GIS)”, “heating demand simulation”, “City Geographical Markup Language (CityGML)”, “Levels of Detail (LoD)”, a further analysis depicted in Figure 3 over the usage of openly available data shows that more than
Suitable for representing individual buildings or urban areas, data models such as CityGML can be used in multiple UBEM-related workflows and tool chains [9]
Summary
In 2018, about 55% of the world’s population resided in urban areas. This number is projected to increase up to 68% by 2050 [1]. At times costly and labour intensive, the technique allows urban planners and simulation scientists to efficiently predict energy trends across large areas by incorporating varied principles and methods of information sciences [7]. The data models used for simulations often vary in their respective geometric and semantic definitions For energy analysis, these models predominantly require additional energy-specific data in the form of enrichment as it lacks essential information required for energy predictions. Due to multiple inconsistencies and limited availability of these models, data interoperability is a critical issue in all stages of a simulation process [9]. Data models such as CityGML are sometimes openly available for a few cities and municipalities [10].
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