Acquisition of Typical Occupancy Schedules for Commercial Buildings from Social Networks
Acquisition of Typical Occupancy Schedules for Commercial Buildings from Social Networks
复制标题
从社交网络获取商业建筑的典型入住表
DOI：
10.1145/3363459.3363537
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
O'Neill, Zheng
中科院分区：
文献类型：
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作者：
Lu, Xing;Feng, Fan;O'Neill, Zheng
Occupancy behavior in buildings has been a hotspot of research as building systems become more sophisticated. Traditional sensing technologies suffer from high costs, inevitable sensor errors, and scalability issues and thus are not widely implemented in buildings.In this notes paper, two different approaches for extracting the typical occupancy schedules for the input to the building energy simulation are explored based on the data from social networks.The first approach is to use text classification algorithms to identify whether people are present in space where they are making posts on social networks. To achieve this, word embedding and machine learning algorithms for the classification are used. On top of that, we could extract the typical occupancy schedules by assuming certain people counting rules. The second approach is to utilize the processed GPS location tracking data provided by social network giants such as Facebook and Google Map. Web scraping techniques are used to obtain the data and extract the building typical occupancy schedules.Two preliminary case studies demonstrate these two approaches as a proof of concept using a museum building, the Art Institute of Chicago. The results show that the extracted building occupancy schedules from different social network data sources (Twitter, Facebook, and Google Map) share a similar trend but slightly distinct with each other, which requires more explorations and further validations and corrections. However, the methodology and promising results from this preliminary study will lay the foundation of the occupancy sensing through the social network data mining, which aims to provide another data source for occupancy sensing in buildings at the building level. This will provide another alternative to estimate the occupancy at the community- and urban-scale.
社交媒体数据在特殊事件人群高分辨率绘图中的应用
DOI：
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发表时间：
2017
期刊：
影响因子：
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作者：
Kelly M. Sims;E. Weber;B. Bhaduri;Gautam Thakur;D. Resseguie
通讯作者：
D. Resseguie
社交媒体能否在建筑入住曲线的发展中发挥作用
DOI：
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发表时间：
2017
期刊：
影响因子：
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作者：
R. Stewart;J. Piburn;E. Weber;M. Urban;A. Morton;Gautam Thakur;B. Bhaduri
通讯作者：
B. Bhaduri