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An energy-cyber-physical system for personalized normative messaging interventions: Identification and classification of behavioral reference groups

机译：用于个性化规范消息的能量 - 网络物理系统：行为参考​​群体的鉴定和分类

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摘要

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.
机译：在居留区内，规范的消息流程干预鼓励家庭从事各种亲环境行为。在基于规范的干预活动中，假设更加亲自相关的参考组增加规范依从性，从而提高了规范性消息传递干预的有效性。先进的能量电网基础设施，如智能电表和云计算，使得通过基于使用模式将家庭与高度相似的用户组动态地分类为具有非侵入性方式，使高度个性化的行为参考组。不幸的是，尚不清楚有关家用能源使用和住房特征的可用数据如何影响动态行为参考组的分类性能。因此，本研究评估了使用易于可用数据的动态行为参考组的分类性能。用于个性化的规范性消息传递干预的能量 - 网络物理系统正在使用密歇根州荷兰荷兰2248户的一年能源使用数据进行培训和测试。动态行为参考组分类证明，每周反馈的准确性为94.7-95.9％，每月反馈仅用于89.9-93.1％，仅使用易于可用的数据。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是以97.6％的精度，召回和F1分数实现的。利用所提出的系统，即使行为模式可能会发生变化，也可以将高度个性化的行为参考组动态分配给家庭的每一个结算周期。因此，介入者将能够大规模部署个性化规范反馈消息。

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