Enriching the 3D City-Model for the Simulation of Urban Heat Demand

In this paper we describe the process of enriching the Hamburg 3D City model (3D-Stadtmodell) with energy relevant attributes for the simulation of heat demand. The 3D data of the city provided by the Landesbetrieb Geoinformation und Vermessung (LGV) is a combination of Cadastre footprints and LiDAR data. This combination of data allows the LGV to produce CityGML data with a level of detail 1 (LOD1). We use this data as basis for the computation of urban heat demand. This paper presents the enrichment process of the CityGML data. We make use of the energy application domain extension (ADE) to store the energy relevant data in a standardized format. For the enrichment process we classify the residential building stock into building types. And classify the non residential sector by use. From the building types we extract heat transmission coefficients of building components. With the enriched 3D city model we perform a monthly heat demand estimation of a selected neighbourhood in Hamburg. The aim of this enrichment process is to create a robust but flexible method for the estimation of heat demand at a neighbourhood level with little energy relevant information. This paper presents a method for a quick estimation of the monthly heat demand of a neighbourhood without the need of any extra data input. This approach can be used by the energy and urban planning community for a first estimation of the heat demand used on a given neighbourhood or the entire city. The results from this approach present an urban heat demand model for the city of Hamburg based on the freely available 3D city model data. Possible uses of this approach are: (1) identification of hot spots in the city, (2) creation of base data sets for the simulation of retrofit scenarios, and (3) creation of temporal heat density maps