Generating a disaggregated synthetic building stock allows for a discrete representation of various building states. Furthermore, the results indicate a separation of the stock in terms of GHG emissions between old fossil fuel-heated buildings and new and refurbished buildings that are heated by renewable energy. These and other indicators vary significantly within common building stock segments that consider only few attributes such as building type and construction period. The results demonstrate that the energy demand and GHG emissions can vary greatly across the stock. The methodology is implemented and validated for the residential building stock of Switzerland. By drawing on relevant research, the developed methodology uses aggregate national data and combines it with various data sources to generate a disaggregated synthetic building stock. This paper introduces a new method of building stock modeling based on the generation of synthetic building stocks. The most common approach to building stock modeling is to construct archetypes that are taken to representing large segments of the stock. A building stock model (BSM) is a method of assessing the energy demand and GHG emissions of building stocks and developing pathways for energy and GHG emission reduction. In Europe, the final energy demand and greenhouse gas (GHG) emissions of residential and commercial building stocks account for approximately 40% of energy and emissions.
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