An energy-cyber-physical system for personalized normative messaging interventions: Identification and classification of behavioral reference groups
An energy-cyber-physical system for personalized normative messaging interventions: Identification and classification of behavioral reference groups
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DOI：
10.1016/j.apenergy.2019.114237
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发表时间：
2020-02
期刊：
影响因子：
11.2
通讯作者：
K. Song;Kyle Anderson;SangHyun Lee
中科院分区：
文献类型：
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作者：
K. Song;Kyle Anderson;SangHyun Lee
在住宅内，规范性的信息干预措施鼓励家庭参与各种有利于环境的行为。在基于规范的干预活动中，人们假设，更多的个人相关的参考群体增加规范的遵守，从而提高规范性信息干预的有效性。智能电表和云计算等先进的电网基础设施能够以非侵入性的方式创建高度个性化的行为参考组，方法是根据使用模式将家庭动态分类为高度相似的用户组。不幸的是，它仍然不清楚如何现成的数据家庭能源使用和住房特征影响动态行为参考组的分类性能。因此，本研究使用现成的数据评估动态行为参考组的分类性能。一个用于个性化规范信息干预的能源网络物理系统使用密歇根州荷兰2248户家庭一年的能源使用数据进行了培训和测试。动态行为参考组分类被证明是非常准确的，94.7-95.9%的每周反馈和89.9-93.1%的每月反馈，只使用现成的数据。此外，使用更多的历史能源使用数据有助于提高分类准确性。最后，每个行为参考组的高分类性能达到97.6%的准确率，召回率和F1分数。通过所提出的系统，即使行为模式可能发生变化，也可以在每个计费周期动态地为家庭分配高度个性化的行为参考群体。因此，干预者将能够大规模部署个性化的规范反馈信息。
Within residences, normative messaging interventions have encouraged households to engage in various pro-environmental behaviors. In norm-based intervention campaigns, it is hypothesized that more personally relevant reference groups increase norm adherence, thus improving the effectiveness of normative messaging interventions. Advanced energy grid infrastructure, such as smart meters and cloud computing, enables the creation of highly personalized behavioral reference groups in a non-invasive manner by dynamically classifying households into highly similar user groups based on usage patterns. Unfortunately, it remains unclear how readily available data on household energy use and housing characteristics affect the classification performance of dynamic behavioral reference groups. Therefore, this research evaluates the classification performance of dynamic behavioral reference groups using readily available data. An energy-cyber-physical system for personalized normative messaging interventions is trained and tested using one-year of energy use data from 2248 households in Holland, Michigan. Dynamic behavioral reference group classification proved very accurate, 94.7–95.9% for weekly feedback and 89.9–93.1% for monthly feedback using only readily available data. In addition, using more historical energy use data contributes to enhancing classification accuracy. Lastly, high classification performance for each behavioral reference group is achieved at 97.6% of precision, recall and F1-score. With the proposed system, it is possible to dynamically assign highly personalized behavioral reference groups to households every billing cycle even if behavioral patterns are subject to change. Thus, interveners will be able to deploy personalized normative feedback messages on a large scale.