A novel mobility-based approach to derive urban-scale building occupant profiles and analyze impacts on building energy consumption
A novel mobility-based approach to derive urban-scale building occupant profiles and analyze impacts on building energy consumption
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DOI：
10.1016/j.apenergy.2020.115656
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发表时间：
2020-11
期刊：
影响因子：
11.2
通讯作者：
Wenbo Wu;B. Dong;Qi Wang;M. Kong;D. Yan;Jingjing An;Yapan Liu
中科院分区：
文献类型：
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作者：
Wenbo Wu;B. Dong;Qi Wang;M. Kong;D. Yan;Jingjing An;Yapan Liu
在美国，人们90%以上的时间都呆在建筑物里，占全国总用电量的70%以上。居住者的行为正成为影响建筑能耗的主要因素。现有的居住者行为研究通常局限于单个建筑和个人行为，例如在密闭空间中的存在或互动。此外，在建筑或社区层面上对居住者行为进行建模的研究是有限的。随着物联网的发展，移动定位数据可以通过社交媒体和基于位置的服务应用获得。本研究的目的是分析来自高分辨率城市规模移动位置数据的更具代表性的占用概况对建筑能耗的影响。一项试点研究在德克萨斯州圣安东尼奥市中心的900多座建筑物上进行，其中有数十亿的移动定位数据。然后，我们将这些剖面与现有的能源部原型模型进行比较，并使用统计方法对差异进行量化。平均而言，根据经验资料得出的入住率与能源部参考资料得出的入住率之间的差异在- 30%到70%之间。然后在CityBES中模拟真实的衍生轮廓。结果表明，预测的制冷能源需求减少了40%，供热能源需求减少了60%。因此，本研究推进了城市规划知识以及城市尺度的能源建模和优化。
In the US, people spend more than 90% of their time in buildings, which contributes to more than 70% of overall electricity usage in the country. Occupant behavior is becoming a leading factor impacting energy consumption in buildings. Existing occupant-behavior studies are often limited to a single building and individual behavior, such as presence or interactions in confined spaces. Moreover, studies modeling occupant behavior at the building or community level are limited. With the development of the Internet of Things, mobile positioning data are available through social media and location-based service applications. The goal of this study is to analyze the impacts of more representative occupancy profiles, derived from high resolution urban scale mobile position data, on building energy consumption. . A pilot study was conducted on more than 900 buildings in downtown San Antonio, Texas, with billions of mobile positioning data. We then compared these profiles with the existing Department of Energy prototype models and quantified the differences using a statistical method. On average, the differences in occupancy rates between the ones derived from the empirical profile and the ones from the Department of Energy reference ranged from −30% to 70%. The realistic derived profiles are then simulated in the CityBES. The results show that the predicted cooling energy demand is reduced by up to 40% while the heating energy demand is reduced by up to 60%. This study, therefore, advances knowledge of urban planning as well as urban-scale energy modeling and optimization.