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F. Tahmasebi, M. Schuss, A. Mahdavi:
"Exploring the effectiveness of window operation models for thermal comfort and energy performance assessments";
Talk: Proceedings of the CESBP Central European Symposium on Building Physics AND BauSIM 2016, Dresden, Germany; 2016-09-14 - 2016-09-16; in: "Proceedings of BauSim 2016", J. Grunewald et al. (ed.); Technische Universität Dresden / Scientific Committee of the CESBP // Fraunhofer IRB Verlag, (2016), 978‐3‐8167‐9798‐2; 489 - 496.

Use of advanced occupancy-related models is gaining momentum in the building simulation community. However, the predictive performance of occupant behavior models in different contexts and their implications for building performance results involve potentially detrimental uncertainties. In this
context, the current contribution deploys long-term monitored data from an office area and its calibrated simulation model to conduct an external evaluation of a number of widely used stochastic and nonstochastic window operation models in view of their
effectiveness to enhance the reliability of thermal comfort and heating demand assessments. The results suggest that, while stochastic models can emulate the seemingly random character of occupant behavior, their use does not guarantee more reliable
predictions. Leaving aside the large errors resulted from using such models without the necessary adjustments, stochastic window operation models overestimated the occupants´ operation of windows
in heating season and thus the annual and peak heating demand. However, as compared with rulebased models, the stochastic models displayed a better performance in window operation prediction
and thermal comfort assessment in the free-running season.

[no german version available] Use of advanced occupancy-related models is gaining momentum in the building simulation community. However, the predictive performance of occupant behavior models in different contexts and their implications for building performance results involve potentially detrimental uncertainties. In this
context, the current contribution deploys long-term monitored data from an office area and its calibrated simulation model to conduct an external evaluation of a number of widely used stochastic and nonstochastic window operation models in view of their
effectiveness to enhance the reliability of thermal comfort and heating demand assessments. The results suggest that, while stochastic models can emulate the seemingly random character of occupant behavior, their use does not guarantee more reliable
predictions. Leaving aside the large errors resulted from using such models without the necessary adjustments, stochastic window operation models overestimated the occupants´ operation of windows
in heating season and thus the annual and peak heating demand. However, as compared with rulebased models, the stochastic models displayed a better performance in window operation prediction
and thermal comfort assessment in the free-running season.