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Building performance discrepancies between building design and operation areone of the causes that lead many new designs fail to achieve their goals andobjectives. One of main factors contributing to the discrepancy is occupantbehaviors. Occupants responding to a new design are influenced by severalfactors. Existing building performance models (BPMs) ignore or partiallyaddress those factors (called contextual factors) while developing BPMs. Topotentially reduce the discrepancies and improve the prediction accuracy ofBPMs, this paper proposes a computational framework for learning mixture modelsby using Generative Adversarial Networks (GANs) that appropriately combiningexisting BPMs with knowledge on occupant behaviors to contextual factors in newdesigns. Immersive virtual environments (IVEs) experiments are used to acquiredata on such behaviors. Performance targets are used to guide appropriatecombination of existing BPMs with knowledge on occupant behaviors. Theresulting model obtained is called an augmented BPM. Two different experimentsrelated to occupant lighting behaviors are shown as case study. The resultsreveal that augmented BPMs significantly outperformed existing BPMs withrespect to achieving specified performance targets. The case study confirms thepotential of the computational framework for improving prediction accuracy ofBPMs during design.

Improving Prediction Accuracy in Building Performance Models Using Generative Adversarial Networks (GANs)