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首页> 外文期刊>Automation in construction >Combining context-aware design-specific data and building performance models to improve building performance predictions during design
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Combining context-aware design-specific data and building performance models to improve building performance predictions during design

机译：结合上下文感知的特定于设计的数据和建筑性能模型，以改善设计期间的建筑性能预测

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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.
机译：诸如建筑能耗模拟模型之类的建筑性能模型（BPM）已广泛用于建筑设计中。传统的BPM可能无法有效解决仍在设计中的新建筑物中人与建筑物的相互作用。缺乏这种能力通常会导致建筑物性能差距的存在，即设计期间预测性能与建筑物实际性能之间的差异。为了提高常规BPM的预测准确性，开发了一种计算框架。它使用机器学习方法将现有的BPM与上下文相关的特定于设计的数据结合在一起，这些数据涉及新设计中人与建筑之间的交互。沉浸式虚拟环境（IVE）用于捕获描述特定于设计的人与建筑物交互作用的数据；人工神经网络（ANN）用于合并从现有BPM和NE获得的数据以生成增强的BPM。此外，该框架还具有使用功能分级技术对影响人与建筑互动的因素的影响进行分级的功能，这可以帮助设计未来的IVE实验以更好地收集数据。该框架使用一个单独的办公室进行了测试。创建办公室的IVE以在设计过程中模拟关键的人造光使用事件。 Hunt模型被选择为现有BPM。使用传感器观察办公室中人工照明的实际使用情况，为期一个月，以验证框架的有效性。该应用程序的结果显示了该框架在提高根据从实际办公室获得的数据评估的Hunt模型的预测准确性方面的潜力。结果证实了上下文相关的设计特定数据在改进设计过程中人与建筑交互的预测中的重要作用。此外，特征分级技术可以有效地识别影响人与建筑互动的因素。还讨论了这项研究和未来工作的局限性。

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