Profiling Occupancy Patterns to Calibrate Urban Building Energy Models (UBEMs) Using Measured Data Clustering
Profiling Occupancy Patterns to Calibrate Urban Building Energy Models (UBEMs) Using Measured Data Clustering
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DOI：
10.1080/24751448.2018.1497369
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发表时间：
2018-07
期刊：
影响因子：
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通讯作者：
Rawad El Kontar;T. Rakha
中科院分区：
文献类型：
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作者：
Rawad El Kontar;T. Rakha
与测量数据相比，预测占用模式的不确定性会导致模拟建筑能源存在差异。典型的模拟模型通过相同的时间表和重复的行为来表示居住者。然而，用户的活动模式包括许多变化，特别是当关注邻近范围内的建筑物中的交互时。城市规模的模拟为设计决策提供了信息，其中一个主要挑战是确定占用行为的可代表输入。本文提出了一个框架，用于建模的占用率和随之而来的能源负荷在住宅建筑使用测量数据进行校准，它采用了功能聚类方法来配置文件的能源使用，生成输入城市能源模型（UBEMS）。该框架在一个住宅小区上进行了验证，并揭示了所产生的输入可以更准确地预测社区能源负荷模式。
Uncertainty in predicting occupancy patterns leads to discrepancies in simulated building energy when compared to measured data. Typical simulation models represent occupants through identical schedules and repetitive behavior. However, users’ activity patterns comprise numerous variations, especially when focusing on interactions in buildings on the neighborhood scale. Urban-scale simulations inform design decisions, and one of the major challenges is identifying representable inputs for occupancy behavior. This paper presents a framework for modeling occupancy and consequent energy loads in residential buildings using measured data for calibration; it employs a functional clustering approach to profile energy use, which generates inputs for Urban Energy Models (UBEMs). The framework is demonstrated on a residential neighborhood and reveals that the generated inputs can more accurately predict community energy load patterns.