Development of a city information model to support data management and analysis of building energy systems within complex city districts

Abstract

ABSTRACTUrbanization causes increasing energy demand within cites. To identify energy savingpotentials on city district scale, integral planning approaches are necessary. This paperdescribes the development of a city information model (CIM) with focus on buildingsand energy systems. It accounts for di erent building and energy system types plusgeometry, building physics as well as occupant information. The CIM structure has beenimplemented on a PostgreSQL database (DB), which has been linked to a geographicinformation system (GIS) to enable graphical data analysis and modi cation. Consideringdata uncertainty, the CIM structure supports a simpli ed city district modeling. Withina case study, DB and a Python tool are used to parameterize buildings for dynamicsimulations to estimate the state of retro t.Keywords: City district, CIM, GIS, energy systems, PostgreSQLINTRODUCTIONThe megatrend urbanization causes increasing needs within cities. Especially rising energydemands have to be covered. This concentrated energy demand leads to high greenhousegas emissions within cities. Urban areas are responsible for around 70% of emissions, butonly cover 2% of land mass [1]. Therefore, they o er great potential for emission reduction.However, the identi cation of options for greenhouse gas emission reduction on city districtscale is challenging. A single building already holds a multitude of parameters, such asbuilding age, type or geometry attributes. A city district can consist of a plurality ofheterogeneous buildings. Therefore, the number of relevant objects and their attributesrises tremendously. The city complexity can complicate data handling, which is necessaryfor the analysis of city districts. Thus, the data management should be simpli ed tosupport the identi cation of greenhouse gas emission options.This paper describes the development of a city information model (CIM) with focus onenergy systems. It accounts for di erent building and energy system types. The demandside is mainly represented by buildings and their user behavior. Therefore, the CIM takesbuilding geometry, building physics as well as occupant or attendee information intoaccount. On the generation and distribution side diverse thermal and electrical energysupply systems are included. The CIM structure has been implemented on a PostgreSQL-Server. The relational database (DB) is linked to the GIS software QGIS, which combinesentity location and further object data. This enables graphical data analysis and modi ca-