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Occupancy data analytics and prediction: A case study

机译：占用数据分析和预测：案例研究

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摘要

Occupants are a critical impact factor of building energy consumption. Numerous previous studies emphasized the role of occupants and investigated the interactions between occupants and buildings. However, a fundamental problem, how to learn occupancy patterns and predict occupancy schedule, has not been well addressed due to highly stochastic activities of occupants and insufficient data. This study proposes a data mining based approach for occupancy schedule learning and prediction in office buildings. The proposed approach first recognizes the patterns of occupant presence by cluster analysis, then learns the schedule rules by decision tree, and finally predicts the occupancy schedules based on the inducted rules. A case study was conducted in an office building in Philadelphia, U.S. Based on one-year observed data, the validation results indicate that the proposed approach significantly improves the accuracy of occupancy schedule prediction. The proposed approach only requires simple input data (i.e., the time series data of occupant number entering and exiting a building), which is available in most office buildings. Therefore, this approach is practical to facilitate occupancy schedule prediction, building energy simulation and facility operation. (C) 2016 Elsevier Ltd. All rights reserved.
机译：居住者是建筑能耗的关键影响因素。先前的许多研究都强调了居住者的作用，并研究了居住者与建筑物之间的相互作用。然而，由于乘员的高度随机活动和数据不足，如何学习乘员模式和预测乘员时间表的基本问题尚未得到很好的解决。这项研究提出了一种基于数据挖掘的办公楼占用计划学习和预测方法。提出的方法首先通过聚类分析识别乘员的存在模式，然后通过决策树学习调度规则，最后根据归纳规则预测乘员调度。在美国费城的一栋办公楼中进行了案例研究。根据一年来的观察数据，验证结果表明所提出的方法大大提高了入住时间表的准确性。所提出的方法仅需要简单的输入数据（即，进入和离开建筑物的人员编号的时间序列数据），这在大多数办公楼中都是可用的。因此，该方法对促进占用时间表的预测，建筑能耗模拟和设施运营非常实用。 （C）2016 Elsevier Ltd.保留所有权利。

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  • 来源
    《Building and Environment》 |2016年第6期|179-192|共14页
  • 作者

    Liang Xin; Hong Tianzhen; Shen Geoffrey Qiping;

  • 作者单位

    Hong Kong Polytech Univ, Dept Bldg & Real Estate, Hong Kong, Hong Kong, Peoples R China|Univ Calif Berkeley, Lawrence Berkeley Natl Lab, Bldg Technol & Urban Syst Div, Berkeley, CA 94720 USA;

    Univ Calif Berkeley, Lawrence Berkeley Natl Lab, Bldg Technol & Urban Syst Div, Berkeley, CA 94720 USA;

    Hong Kong Polytech Univ, Dept Bldg & Real Estate, Hong Kong, Hong Kong, Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Occupancy prediction; Occupant presence; Data mining; Machine learning;

    机译：占位预测;占位者;数据挖掘;机器学习;

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