Hofmann, Joachim WernerAmoser, ChristianGeissler, AchimHall, Monika2023-12-072023-12-072023-12-011742-65881742-659610.1088/1742-6596/2600/3/032013https://irf.fhnw.ch/handle/11654/38816https://doi.org/10.26041/fhnw-5920The renovation rate of existing buildings plays a major role in the Swiss Energy Strategy 2050+. To increase this rate, there must be a simple and cost-effective method to determine the heat demand of existing buildings. In this paper, the generation of such a method, based on the Swiss cantonal building energy certificate (GEAK) database with the help of machine learning (ML), is studied. The aim of the project was to develop a ML model which allows the heat demand of existing buildings to be determined quickly with a minimal set of parameters. The comparison of the GEAK building envelope class for single family houses calculated with the new ML model and the original GEAK classes shows that approximately 62 % have the same class, 32 % differ by one class and 6 % by two classes. The ML model is a good starting point for further refinements and developments.enMachine learningDeep neural networkHead demand624 - Ingenieurbau und Umwelttechnik01A - Beitrag in wissenschaftlicher Zeitschrift1-7Institut Nachhaltigkeit und Energie am Bau, Hochschule für Architektur, Bau und Geomatik FHNW