Robustness analysis framework for computations associated with building performance models and immersive virtual experiments

Abstract

Building performance models (BPMs) have been used to simulate and analyze building performance during design. While extensive research efforts have made to improve the performance of BPMs, little attention has given to their robustness. Uncertainty is a crucial factor affecting the robustness of BPMs, in which such effect needs to be quantified through a suitable approach. The paper offers a robustness analysis framework for BPMs by using perturbation techniques to simulate uncertainty in input datasets. To investigate the efficacy of the framework, a generative adversarial network (GAN)-based framework was selected as a case study to analyze light switch usages in a single-occupancy office simulated using an immersive virtual environment (IVE). The robustness of the GAN was analyzed by comparing differences between a baseline (i.e., a BPM obtained from the GAN trained on a non-perturbed dataset) and BPMs obtained from the GAN trained on perturbed datasets. Overall, the robustness of the GAN significantly reduced when the training datasets were perturbed by using structured transformation techniques. The GAN remained relatively robust when the training datasets were perturbed by using an additive perturbation. Additionally, the sensitivity of the GAN involves different magnitudes corresponding to different levels of perturbed input datasets. The study suggests that the perturbation analysis is effective for investigating data uncertainty affecting the robustness of BPMs.