Abstract 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.