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F. Tahmasebi, A. Mahdavi:
"An inquiry into the reliability of window operation models in building performance simulation";
Building and Environment, 105 (2016), 343 - 357.

Given the impact of inhabitants´ control actions on indoor environment and the complex nature of such
interactions, sophisticated models of occupants´ presence and behavior are increasingly deployed to
enhance the reliability of building performance simulations. However, the use of occupant behavior
models in building simulation efforts and their predictive performance in different contexts involves
potentially detrimental uncertainties. To address this issue, the present study deploys long-term
monitored data from an office area and its calibrated simulation model to conduct an external evaluation
of a number of stochastic and non-stochastic window operation models in view of their a) potential
in predicting occupants´ operation of windows, and b) effectiveness to enhance the reliability of building
performance simulation efforts. The results suggest that, while stochastic models can emulate the
seemingly random character of occupant behavior and provide probabilistic distributions of performance
indicators, 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 demands. However, as compared with rule-based models, the stochastic models displayed a
better performance in predicting window operations and thermal comfort assessment in the freerunning
season.

(kein deutscher Abstract verfügbar) Given the impact of inhabitants´ control actions on indoor environment and the complex nature of such
interactions, sophisticated models of occupants´ presence and behavior are increasingly deployed to
enhance the reliability of building performance simulations. However, the use of occupant behavior
models in building simulation efforts and their predictive performance in different contexts involves
potentially detrimental uncertainties. To address this issue, the present study deploys long-term
monitored data from an office area and its calibrated simulation model to conduct an external evaluation
of a number of stochastic and non-stochastic window operation models in view of their a) potential
in predicting occupants´ operation of windows, and b) effectiveness to enhance the reliability of building
performance simulation efforts. The results suggest that, while stochastic models can emulate the
seemingly random character of occupant behavior and provide probabilistic distributions of performance
indicators, 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 demands. However, as compared with rule-based models, the stochastic models displayed a
better performance in predicting window operations and thermal comfort assessment in the freerunning
season.

building performance simulation, occupant behavior, window operation models, external validation, building performance indicators