Application of occupant behavior prediction model on residential big data analysis
Application of occupant behavior prediction model on residential big data analysis
复制标题
居住者行为预测模型在住宅大数据分析中的应用
DOI：
10.1145/3486611.3491121
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Zhao, Dong
中科院分区：
文献类型：
--
作者：
Mo, Yunjeong;Zhao, Dong
乘员行为是多方面的，需要一种系统的方法来全面了解乘员行为。本研究的目的是定义一个结构之间的关系，能源消耗，建筑技术和居住者行为，使用的居住者行为预测模型。该模型可以预测和解释居住者能源使用相关的活动。使用机器学习方法来开发模型，并使用美国时间使用调查（American Time Use Survey，缩写为ESTA）的数据集来验证模型。结果表明，预测性能较高的能源使用活动比预测性能较低的能源使用活动更稳定和习惯。该模型对这些习惯性活动的预测准确率高达99%。研究结果表明，建筑系统和控制策略需要调整，以适应习惯的能源使用行为，而不是改变的行为。此外，教育干预似乎更有效的不习惯的行为，这往往会改变。
Occupant behavior is multifaceted, and a systematic approach is required to understand occupant behavior comprehensively. This research aims to define a structure of the relationship between energy consumption, building technology, and occupant behavior, using the Occupant Behavior Prediction Model. The model can predict and explain occupant energy usage-related activities. A machine learning approach is used to develop the model, and datasets from the American Time Use Survey (ATUS) are used to verify the model. The results show that the energy use activities with higher predictive performances are more stable and habitual compared to the ones with lower predictive performances. The prediction accuracy achieved by this model for these habitual activities reached as high as 99%. The findings imply that the building systems and control strategies need to be adjusted to accommodate habitual energy use behaviors, rather than changing the behaviors. In addition, educational interventions seem more effective on the less habitual behaviors, which often change.
DOI：
--
发表时间：
2014
期刊：
Journal of management science
影响因子：
--
作者：
อนิรุธ สืบสิงห์
通讯作者：
อนิรุธ สืบสิงห์
影响因子：
6.4
作者：
Yunjeong Mo;Dong Zhao
通讯作者：
Yunjeong Mo;Dong Zhao