Occupants' interactions with the building envelope and building systems can have a large impact on the indoor environment and energy consumption in a building. As a consequence, any realistic forecast of building performance must include realistic models of the occupants' interactions with the building controls (windows, thermostats, solar shading etc.). During the last decade, studies about stochastic models of occupants' behaviour in relation to control of the indoor environment have been published. Often the overall aim of these models is to enable more reliable predictions of building performance using building energy performance simulations (BEPS). However, the validity of these models has only been sparsely tested. In this paper, stochastic models of occupants' behaviour from literature were tested against measurements in five apartments. In a monitoring campaign, measurements of indoor temperature, relative humidity and CO2 concentration was measured in the living room and bedroom at five minute intervals in five apartments with similar layout in a building located in Copenhagen, Denmark. Outdoor temperature, relative humidity, wind speed and solar radiation were obtained from a weather station close by. The stochastic models of window opening and heating set-point adjustments were implemented in the BEPS tool IDA ICE. Two apartments from the monitoring campaign were simulated using the implemented models and the measured weather data. The results were compared to measurements from the monitoring campaign to get an estimate of the forecast's realism. The simulations resulted in realistic predictions in a sense that the measured values were within or close to the range of the simulated values. The variation in the simulated and measured variables between apartments and over time was similar. However, comparisons of the average stochastic predictions with the measured temperatures, relative humidity and CO2 concentrations revealed that the models did not predict the actual indoor environmental conditions well.
- Building energy performance simulation
- Heating set-point adjustments
- Stochastic models
- Window opening behaviour