Lowering energy demand in buildings is high on the political agenda considering household energy consumption accounts for 22% of the total final energy usage in the Netherlands. Tools that can run these types of simulations for an entire city are required for effective policy design to lower energy consumption within the built environment. This thesis explores how to build such a tool that allows for space heating demand to be computed that follows the NTA 8800 norm. Therefore, the main research question is: “To what extent can a heat demand model be developed that adapts and implements the NTA 8800 to be coupled with CityGML-based semantic 3D city models?”. The thesis uses a mixed-method research approach of qualitative concept mapping and quantitative modelling to build a space heating demand model for urban analysis. The final product is a Python-based model with database interactions, compatible with CityGML-based semantic 3D city models, following the NTA 8800 computation methods. Initial testing on two buildings highlighted areas for model development, including the underestimating of ventilation losses and the omission of windows and doors in transmission estimates. Improved window quality could result in significant energy savings, according to the solar gain analysis. The model’s capacity to mimic anticipated trends in space heating demand was verified using benchmark data, although it deviated from previous consumption patterns, indicating the need for additional research. The model was tested for Rijssen-Holten and produced rapid results within 5 minutes. Despite limitations due to data availability and model assumptions, the thesis model underscores the utility of Geomatics in urban energy management and the results can help design energy policy design for sustainable development.