ACoolHeaD: Framework for Automated Cooling and Heating Demand calculations using spatially and temporally resolved building performance simulations applied to the estimation of heating demand in Germany

Abstract Reducing the emissions in the building sector requires reasonable renovation and integration of renewable energy sources to find the optimal trade-off between ecological benefit and economic effort. However, many buildings, different types of usage, needs of residents, and transient weather conditions contribute to a vast number of use cases, making the search for the optimal trade-off challenging. Therefore, a systematic reduction of use cases is necessary to target emission reductions in the building sector. Furthermore, emissions in the building sector mainly belong to heating and cooling purposes. Thus, many frameworks exist to estimate the heating and cooling demand at building and district scales. In addition, some contributions exist at a national scale, determining spatially resolved demands. However, the optimal integration of fluctuating renewable energy sources requires a detailed resolution in the time domain. Currently, no framework allows the calculation of spatially and temporally resolved demands at the national scale from which a meaningful use case reduction can be obtained. This work proposes the ACoolHeaD framework for A utomated Coo ling and Hea ting D emand calculations for entire urban districts and countries based on spatially and temporally resolved building performance simulations. ACoolHeaD connects predefined formatted input data with the TEASER tool for automated parameterization of building simulation models. After parameterizing the building models, the framework selects representative weather data sets as boundary conditions for the simulation to ensure spatial and temporal resolution during computation. The calculation of the demands is fully automated. Finally, the k-medoids clustering algorithm is applied to systematically reduce the number of use cases, identifying representative demands. As a use case for ACoolHeaD serves Germany since sound data available. Thus, ACoolHeaD estimates the representative heating demand for about 19 million buildings in Germany in this work. An extended input data preparation reduces the computational effort from about 19 million building simulations to 3,520. The average, maximum, and integral heating demand is determined in each simulation, which are classification indicators for the clustering algorithm. Germany’s integral overall (clustered) heating demand is estimated at 2,796 PJ (2,793 PJ), respectively, which is about 10 % higher than the current values of the Federal Ministry (2,557 PJ). The result shows a good agreement and a successful application of ACoolHeaD considering all necessary assumptions. Based on the maximum and average heating demand, the entire building stock is clustered by k-medoids clustering to five buildings, which can be used for further representative investigations considering Germany. As a next step, we recommend investigating the influence of occupancy, the selected weather data sets, and analyzing cooling for future scenarios to increase the detail level and thus improve the expressiveness of ACoolHeaD results.