Building occupancy, one of the most important consequences of occupant behaviors, is a driving influencer for building energy consumption and has been receiving increasing attention in the building energy modeling community. With the vast development of information technologies in the era of the internet-of-things, occupant sensing and data acquisition are not limited to a single node or traditional approaches. The prevalence of social networks provides a myriad of publically available social media data that might contain occupancy information in the space for a given time. In this paper, we explore two approaches to extract the typical occupancy schedules for the input to the building energy simulation based on the data from social networks. The first approach uses text classification algorithms to identify whether people are present in the space where they are posting on social media. On top of that, the typical building occupancy schedules are extracted with assumed people counting rules. The second approach utilizes the processed Global Positioning System (GPS) tracking data provided by social networking service companies such as Facebook and Google Maps. Web scraping techniques are used to obtain and post-process the raw data to extract the typical building occupancy schedules. The results show that the extracted building occupancy schedules from different data sources (Twitter, Facebook, and Google Maps) share a similar trend but are slightly distinct from each other and hence may require further validation and corrections. To further demonstrate the application of the extracted Typical Occupancy Schedules from Social Media (TOSSM), data-driven models for predicting hourly energy usage prediction of a university museum are developed with the integration of TOSSM. The results indicate that the incorporation of TOSSM could improve the hourly energy usage prediction accuracy to a small extent regarding the four adopted evaluation metrics for this museum building.

占用率是占用者行为的最重要后果之一，它是影响建筑能耗的重要因素，并且在建筑能耗建模界越来越受到关注。随着物联网时代信息技术的飞速发展，乘员感知和数据采集不限于单个节点或传统方法。社交网络的普及提供了无数公开可用的社交媒体数据，这些数据可能在给定时间内包含空间中的占用信息。在本文中，我们探索了两种基于社交网络数据提取典型占用时间表以输入建筑能耗模拟的方法。第一种方法使用文本分类算法来识别人们是否存在于他们在社交媒体上发布的空间中。最重要的是，在假定的人员计数规则的基础上提取典型的建筑入住时间表。第二种方法利用由社交网络服务公司（例如Facebook和Google Maps）提供的经过处理的全球定位系统（GPS）跟踪数据。Web抓取技术用于获取原始数据并对其进行后处理，以提取典型的建筑物入住时间表。结果表明，从不同数据源（Twitter，Facebook和Google Maps）提取的建筑物占用时间表具有相似的趋势，但彼此之间略有不同，因此可能需要进一步的验证和更正。为了进一步说明从社交媒体（TOSSM）中提取的典型占用时间表的应用，与TOSSM的集成开发了用于预测大学博物馆的每小时能耗预测的数据驱动模型。结果表明，就该博物馆建筑采用的四个评估指标而言，TOSSM的并入可以在较小程度上提高小时能耗预测的准确性。