CAREER: Model-based Energy Management for Sustainable Buildings

现代社会在很大程度上依赖于大量的？肮脏的，？能量来维持自己。 总的来说，建筑物消耗的能源比其他广泛的消费部门更多。提高建筑物能源效率的一个重要障碍是，大规模电力负荷的细粒度监测仍然不切实际：它是昂贵的，侵入性的，不可靠的。 我们的假设是，描述负载如何使用电力的详细模型的综合存储库为以编程方式管理建筑能耗提供了基础。 项目？的目标是推进研究和教育，使用基于模型的范例，设计自动化的负载发现，监控和调度技术，成本低，高度透明，隐私保护。 为了实现这一目标，该项目将引导和播种一个在线数据存储库，以实现基于能源使用的负荷建模新技术。 由于存在多种类型的负载，我们的目标是开发一个负载建模技术库。 然后，该项目将利用这些模型自动发现建筑物运行的负载集，并仅使用建筑物智能电表记录的总电量来监控其用电量。 为了使学生受益，该项目将开发关于建筑物能源管理的新课程，并向公众提供材料，使教育工作者受益。 重要的是，这些课程将使学生获得实践经验？编程？真实的建筑。 这种边做边学的方法非常有效-通过计算机简单地打开和关闭设备是令人大开眼界的-并将成为将本科生纳入研究的催化剂。

Modern society depends on a massive amount of, largely ?dirty,? energy to sustain itself. Collectively, buildings consume more energy than the other broad sectors of consumption. A significant barrier to improving building energy efficiency is that fine-grained monitoring of electrical loads at large scales remains impractical: it is expensive, invasive, and unreliable. Our hypothesis is that a comprehensive repository of detailed models that describe how loads use power provides a foundation for programmatically managing building energy consumption. The project?s goal is to advance research and education using the model-based paradigm to design automated load discovery, monitoring, and scheduling techniques that are low in cost, highly transparent, and privacy preserving. To achieve this goal, the project will bootstrap and seed an online data repository to enable new techniques for modeling loads based on their energy usage. Since many types of loads exist, our goal is to develop a library of load modeling techniques. The project will then leverage these models to automatically discover the set of loads a building operates, and monitor their electricity usage using only aggregate electricity recorded by a building smart meter. To benefit students, the project will develop new courses on building energy management, and make the materials publicly available to benefit educators. Importantly, the courses will enable students to gain hands-on experience by ?programming? real buildings. This learning-by-doing approach is highly effective---simply turning devices on and off via computer is eye-opening---and will serve as a catalyst for including undergraduates in research.