SCC-IRG Track 2: Smart & Connected Kids for Sustainable Energy Communities

摘要 对广泛放弃能源使用的解释通常基于这样一种信念，即能源行为植根于个人对环境和/或能源成本的担忧。这些解释忽略了理解和解决能源行为中的社会学和心理因素的需要。该项目在加利福尼亚州弗里蒙特社区采用了一系列能源使用干预措施，吸引了该社区的儿童、青少年及其家庭。这些干预措施利用了新的智能电网技术，特别是现在通过智能计量和日益智能的设备和电器提供的高分辨率能源数据。对能源行为的新颖的数据驱动理解将允许根据家庭的能源使用生活方式对家庭进行细分。高分辨率数据揭示的生活方式模式、数据驱动的定制反馈和基于社区的干预措施以新颖的方式交织在一起，有可能以经济和环境问题无法做到的方式吸引家庭和社区成员的关注。社区干预措施将以儿童和青少年为重点，并在多个层面上进行，其中：（1）通过移动应用程序向参与者提供数据驱动的可视化和消息； (2) 与当地女童子军进行深入的多次会议干预，鼓励参与实时智能电表能源数据、设备和电器； (3) 当地高中之间的竞赛，以鼓励广泛的能源参与和节约； (4) 由经过培训的社区学院学生主持的社区会议，采用协商投票®技术； (5) 对生活方式和网络效应的复杂分析； (6) 优化建模，通过行为修改更好地了解未来的负载灵活性。有关家庭能源使用的智能电表数据将是人们感兴趣的主要成果。随机实验和参与者调查也将用于衡量该计划的有效性。因此，我们将把工程中的大数据分析和可视化的最佳实践与行为科学中的社区参与干预策略相结合，以增强我们对大数据、社会科学理论和基于社区的方法在当今和未来改变当地能源管理和消费方面的潜在关键作用的认识。

AbstractExplanations of widespread disengagement with energy use often rest on the belief that energy behavior is rooted in individuals' concerns about the environment and/or costs of energy. Missed in these explanations is the need to understand and address sociological and psychological factors in energy behavior. This project employs a series of energy use interventions in the community of Fremont, California, engaging children, youth and their families in this community. The interventions take advantage of new smart grid technologies, in particular, high-resolution energy data now available via smart metering and increasingly smart devices and appliances. Novel data-driven understandings of energy behavior will permit segmenting households according to their energy-use lifestyles. A novel intertwining of revealed lifestyle patterns from high-resolution data, data-driven tailored feedback, and community-based interventions has the potential to attract the hearts and minds of households and community members in ways that economic and environmental concerns cannot. The community interventions will be child- and youth-focused and delivered at multiple levels, with: (1) data-driven visualizations and messages to participants via mobile applications; (2) an in-depth, multi-session intervention with local Girl Scout troops that encourages engagement with real-time smart meter energy data, devices and appliances; (3) a competition among local high schools to encourage widespread energy engagement and savings; (4) community meetings led by trained community college students adapted from Deliberative Polling® techniques; (5) sophisticated analytics of lifestyle and network effects; and (6) optimization modeling to better understand future load flexibility with behavioral modifications. Smart meter data on household energy use will be the main outcome of interest. Randomized experiments and participant surveys will also be used to gauge the program's effectiveness. We will thus combine the best practices of big data analytics and visualization from engineering with community-engaged intervention strategies from the behavioral sciences to enhance our knowledge of the potential pivotal role of big data, social science theory, and community-based methods in transforming local energy management and consumption today and in the future.