THE IMPACT OF USER AND SYSTEM ASSUMPTIONS ON ENERGY SIMULATION RESULTS Kristina Kiesel, Kristina Orehounig, Ardeshir Mahdavi Department of Building Physics and Building Ecology Vienna University of Technology Vienna, Austria ABSTRACT Using the example of an existing office building, the present paper explores the influence of different simulation input assumptions such as set point temperatures and ventilation behavior on the heating load of a building. Moreover, heating load simulation results with empirically-based input assumptions are compared with simulation that use standardized input assumptions. INTRODUCTION The heating load of a building is influenced by several factors, the building itself (geometry, construction, layout, mechanical equipment), the climate, the location, and the users. It is important to know the influence these assumptions have on the predicted heating load of a building, if a building's thermal performance is to be optimized through a simulation-supported building design process. In this context, the first objective of this paper is to explore the influence of different simulation input assumptions such as set point temperatures and ventilation behavior on the heating load of a building. The second objective is to compare the extent to which observation-based and code-based simulation assumptions lead to different results in terms of predicted heating load magnitudes. METHOD To pursue the above objectives, the following approach was taken: First, an existing typical office building in Hartberg, Austria was selected as the case in point (Mahdavi 2008). We focused on the thermal performance of six office spaces in this office building (R1 to R6). Figure 1 shows the northeast facing façade with the location of the observed offices. Figure 2 shows a schematic floor plan. The offices were located on the first and second floor of the building. They were either single or double occupancy with two to three manually operated windows, manually operated radiators and four to six luminaries. No ventilation or air conditioning system was installed. The workstations were equipped with desktop computers and in some cases printers. Subsequently, the indoor conditions in these offices (air temperature and relative humidity, illuminance) were monitored together with user presence and actions (opening and closing of windows, deployment of shades, switching the lights on and off) (Mahdavi 200/). In addition, a weather station was mounted on top of the building collecting information about outdoor temperature and relative humidity, as well as global horizontal irradiance and illuminance. The data was measured and stored every five minutes. Window opening and shading was monitored via time-lapse digital photography: the degree of shade deployment for each office was derived based on regularly taken digital photographs of the façade. The observation period was from mid November 2005 to July 2006. Figure 1 Façade of the building and location of observed offices Figure 2 Schematic floor plan of sample offices in Hartberg