Combining context-aware design-specific data and building performance models to improve building performance predictions during design
Combining context-aware design-specific data and building performance models to improve building performance predictions during design
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DOI：
10.1016/j.autcon.2019.102917
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发表时间：
2019-11-01
影响因子：
10.3
通讯作者：
Mukhopadhyay, Supratik
中科院分区：
文献类型：
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作者：
Chokwitthaya, Chanachok;Zhu, Yimin;Mukhopadhyay, Supratik
建筑绩效模型（BPM）（例如建筑能源模拟模型）已广泛用于建筑设计。传统的BPM可能无法有效解决仍在设计的新建筑物中的人类建设互动。缺乏这种能力通常会导致建筑绩效差距的存在，即在设计期间预测性能与建筑物的实际性能之间存在差异。为了提高常规BPM的预测准确性，开发了一个计算框架。它使用机器学习方法将现有的BPM与上下文感知的特定于设计的数据相结合，涉及新设计中的人类构建相互作用。沉浸式虚拟环境（IVE）用于捕获描述特定设计的人类建设相互作用的数据；人工神经网络（ANN）用于组合从现有BPM和NE获得的数据，以产生增强的BPM。此外，该框架具有使用功能排名技术对影响人类建设相互作用的因素的影响的能力，该技术可以帮助设计未来的IVE实验以进行更好的数据收集。使用单个占用办公室的应用对框架进行了测试。创建了办公室的IVE，以模拟设计过程中的关键人工光使用事件。选择狩猎模型作为现有的BPM。使用传感器可以在办公室中实际使用人工照明，以验证框架的有效性。该应用程序的结果表明，该框架的潜力在提高了根据实际办公室获得的数据评估的狩猎模型的预测准确性。结果验证了环境感知设计特定数据在改善设计过程中人类建设相互作用的预测中的重要作用。此外，功能排名技术可有效识别影响人类建设相互作用的影响因素。还讨论了这项研究和未来工作的局限性。
Building performance models (BPMs) such as building energy simulation models have been widely used in building design. Conventional BPMs may not be able to effectively address human-building interactions in new buildings still under design. The lack of such capability often contributes to the existence of building performance gaps, i.e., differences between predicted performance during design and actual performance of buildings. To improve the prediction accuracy of conventional BPMs, a computational framework is developed. It combines an existing BPM with context-aware design-specific data involving human-building interactions in new designs, using a machine learning approach. Immersive virtual environment (IVE) is used to capture data describing design-specific human-building interactions; and an artificial neural network (ANN) is used to combine data obtained from an existing BPM and an NE to produce an augmented BPM. Additionally, the framework has the capability to rank influence of factors impacting human-building interactions using a feature ranking technique, which can help the design of future IVE experiments for better data collection.The framework is tested using an application of a single occupancy office. An IVE of the office is created to simulate key artificial light use events during design. The Hunt model is selected as an existing BPM. The actual use of artificial lighting in the office is observed for one month using sensors to validate the effectiveness of the framework. The results of the application have shown the potential of the framework in improving the prediction accuracy of the Hunt model evaluated against data obtained from the actual office. The results verify the important role of context-aware design-specific data in improving the prediction of human-building interactions during design. In addition, the feature ranking technique is effective in identifying influencing factors impacting human-building interactions. Limitations of this study and future work are also discussed.