  Building performance discrepancies between building design and operation are
one of the causes that lead many new designs fail to achieve their goals and
objectives. One of main factors contributing to the discrepancy is occupant
behaviors. Occupants responding to a new design are influenced by several
factors. Existing building performance models (BPMs) ignore or partially
address those factors (called contextual factors) while developing BPMs. To
potentially reduce the discrepancies and improve the prediction accuracy of
BPMs, this paper proposes a computational framework for learning mixture models
by using Generative Adversarial Networks (GANs) that appropriately combining
existing BPMs with knowledge on occupant behaviors to contextual factors in new
designs. Immersive virtual environments (IVEs) experiments are used to acquire
data on such behaviors. Performance targets are used to guide appropriate
combination of existing BPMs with knowledge on occupant behaviors. The
resulting model obtained is called an augmented BPM. Two different experiments
related to occupant lighting behaviors are shown as case study. The results
reveal that augmented BPMs significantly outperformed existing BPMs with
respect to achieving specified performance targets. The case study confirms the
potential of the computational framework for improving prediction accuracy of
BPMs during design.
