Automated urban energy system modeling and thermal building simulation based on OpenStreetMap data sets

Abstract

City districts have a large potential to reduce greenhouse gas emissions by usage of energy efficiency measures. Urban energy models (UEM) can be useful to analyze the impact of different energy efficiency actions on city districts. While simulation of demand data with high spatial and temporal resolution is often necessary to evaluate retrofit measures, the city's complex structure and lack of data often prevents a reliable application of such methods. This paper presents an urban energy modeling approach based on open-source geographical information system (GIS) datasets to reduce input data uncertainty and simplify city district modeling. We present a method to automatically extract basic city district data from OpenStreetMap (OSM) and enrich these datasets based on national building stock statistics. Building models with representative geometries and physical properties are automatically generated based on building archetype information. These models enable thermal simulation on urban scale. The approach is demonstrated for a use case in Germany, where a reference city district model has been generated with OSM data extraction and enrichment. The reference city district model has been used to perform a space heating net energy demand uncertainty analysis. The demand values simulated with the reference model show a sufficient fit with measured consumption values. The approach provides a fast and structured methodology to model city districts and simulate space heating energy demand on urban scale.