Improving Prediction Accuracy in Building Performance Models Using Generative Adversarial Networks (GANs)
Improving Prediction Accuracy in Building Performance Models Using Generative Adversarial Networks (GANs)
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DOI：
10.1109/ijcnn.2019.8852411
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
Chanachok Chokwitthaya;Edward Collier;Yimin Zhu;S. Mukhopadhyay
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文献类型：
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作者：
Chanachok Chokwitthaya;Edward Collier;Yimin Zhu;S. Mukhopadhyay
建筑设计和运营之间的建筑性能差异是领导许多新设计的原因之一，无法实现其目标。造成差异的主要因素是居住行为。响应新设计的乘员受几个因素的影响。在开发BPM时，现有的建筑绩效模型（BPM）忽略或部分解决了这些因素（称为上下文因素）。为了减少差异并提高BPM的预测准确性，本文提出了一种通过使用生成的对抗网络（GAN）来学习混合模型的计算框架，该框架将现有BPM与乘员行为的知识相结合到新设计中的情境因素。沉浸式虚拟环境（IVE）实验用于获取有关此类行为的数据。绩效目标用于指导现有BPM与乘员行为知识的适当组合。获得的模型称为增强BPM。与乘员照明行为有关的两个不同的实验显示为案例研究。结果表明，在实现指定的性能目标方面，增强BPM的表现明显优于现有的BPM。案例研究证实了计算框架在设计过程中提高BPM的预测准确性的潜力。
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. A main factor 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 confirmed the potential of the computational framework for improving prediction accuracy of BPMs during design.