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It is widely accepted that the prediction of building energy performance is strongly related to the occupancy parameters. Currently, existing buildings and laboratories are the main sources for collecting occupancy related data. However, using such data for predicting the energy consumption of future buildings can create a considerable amount of uncertainties. Recent studies show that Immersive Virtual Environments (IVEs) have the potential to generate design and context sensitive occupant-related data. However, extended observations (longitudinal data covering relevant spatial and temporal events) which are necessary for developing quantitative predictive models are impractical using conventional IVEs. To that end, the authors propose a Spatial-Temporal Event

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 occupants 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.Index Terms-occupant behavior, mixture model, building performance model, generative adversarial network, immersive virtual reality

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