Automated Generation of Building Stock Databases and High-Resolution Heat Load Profiles for Districts and Municipalities in Germany

Abstract

Reliable data about the building stock and its heating demand are essential for deciding about the investments in the transformation of the heating sector. Following the practical requirements of planning and engineering offices, we designed a method to automatically generate heat demand and building energy models for entire municipalities or districts in Germany. In this contribution, we present a compilation of data, tools, and methods that enable the generation of a building stock database with building-level spatial resolution and energy demand models that can be simulated in up to hourly resolution. Sources are data about LOD2 geometry, cadastre, building archetypes, and typical weather conditions that are fully open data throughout Germany, and a Random Forest method to inform construction periods for each building. An exemplary application to a small town gives insights about heat demand outcomes as well as about adaptations that were necessary to get consistent results.