With the increasing integration of multiple energy carriers in district energy systems, an accurate simulation of the district energy demand becomes more crucial. To reduce the required computational power, the district energy demand is often quantified through a limited set of archetype buildings, representing the whole district. As the temporal behaviour is important to assess district energy systems, stochastic occupant models should be included in the simulation. However, obtaining representative set-point temperature profiles for archetype buildings is not straightforward, as all buildings respond differently to their demanded set-point temperature due to their thermal inertia. Hence, this paper proposes and compares three techniques to obtain the representative occupant behaviour for the archetype building, by focussing on 847 single-family dwellings. Including a smart occupant aggregation method allows to decrease the percentage error in annual energy demand for space heating between the full and the archetype simulation from around 7.5% to around 2%, depending on the building model and evaluation method.

随着区域能源系统中多种能源载体的集成度越来越高，准确模拟区域能源需求变得越来越重要。为了减少所需的计算能力，通常通过代表整个地区的一组有限的原型建筑来量化地区的能源需求。由于时间行为对于评估区域能源系统很重要，因此模拟中应包括随机乘员模型。但是，要获得原型建筑的代表性设定点温度曲线并非易事，因为所有建筑物由于其热惯性对它们所需的设定点温度的反应不同。因此，本文提出并比较了三种获得原型建筑代表性乘员行为的技术，着重于847个单户住宅。包括智能乘员聚集方法，可将建筑物模拟和原型模拟之间的空间供暖年度能源需求的百分比误差从7.5％降低至2％左右，具体取决于建筑模型和评估方法。