CSR: Small:Collaborative Research: System Support for Green Homes

鼓励更广泛地采用可再生能源是减少我们对电网依赖的关键。虽然在家里安装太阳能电池板越来越普遍，但管理它们产生的能量的过程是手动的和临时的。在这项工作中，pi正在建立一个基础设施，用于收集和分析绿色住宅中的能源产生、能源消耗和用户行为；并利用基于推荐的新型评估平台验证了绿色家庭能源管理的综合方法。使用定制的测量基础设施，该项目对各种可再生能源供电的家庭进行了广泛的研究。这项研究既考察了可再生能源的发电，也考察了各种设备如何以及为什么消耗能源。研究结果为整体控制系统的设计提供了信息，该系统可以将家庭中分布式设备的预测供应与需求相匹配。此外，该系统正在一些家庭中部署，并使用作为移动应用程序实现的推荐系统进行评估，该系统建议用户何时运行设备。该项目支持对鼓励在家庭中采用更环保的做法至关重要的研究，并促成在EPSCoR机构任教的PI Banerjee和在一所本科机构任教的PI Rollins之间的合作。此外，该项目正在开发一门CS1课程，通过收集和分析能源消耗数据，引入计算技术，提高人们对绿色能源概念的认识。

Encouraging broader adoption of renewable energy sources is key to minimizing our dependence on the electric grid. Though installation of solar panels at homes is increasingly common, the process of managing the energy they generate is both manual and ad hoc. In this work, the PIs are building an infrastructure for data collection and analysis of energy generation, energy consumption, and user behavior in green homes; and an an integrated approach to green home energy management validated using a novel recommendation-based evaluation platform. Using a custom-built measurement infrastructure, the project conducts a broad study of homes powered by a variety of renewable sources. The study examines both energy generation by renewable sources as well as how and why energy is consumed by a variety of devices. The results inform the design of a holistic control system that matches predicted supply with demand of a distributed set of devices in the home. Moreover, the system is being deployed in a few homes and evaluated using a recommendation system implemented as a mobile application that suggests when users should run devices.This project supports research critical to encouraging adoption of more environmentally responsible practices in the home and enables a collaboration between PI Banerjee, who teaches at an EPSCoR institution, and PI Rollins, who teaches at an undergraduate institution. Further, the project is developing a CS1 course that will increase awareness of green energy concepts by introducing computing through collection and analysis of data on energy consumption practices.