CSR: EAGER: Design and Implementation of a Fine-Grained Appliance Energy Profiling System for Green Building

CSR：EAGER：绿色建筑细粒度电器能源分析系统的设计和实施

• 批准号: 1255965
• 负责人: Nirmalya Roy
• 金额: $ 26.53万
• 依托单位: Washington State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-01-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1255965&HistoricalAwards=false
• 关键词: CSR; EAGER; Design; Implementation; Fine

Green building applications need efficient and fine-grained determination of power consumption pattern of a wide variety of consumer-grade appliances through non-intrusive load monitoring (NILM) techniques for an effective adaptation and percolation of demand response model down to the consumer level appliances. A key inhibitor to the widespread adoption of such demand response policy at the consumer grade appliances for intelligent building energy management, is the inability of smart plug to efficiently determine, control or infer the power consumption pattern of multiple devices in tandem. In practice, deploying smart plug based NILM and acquiring the low-level power measures of a large number of devices is often difficult or impossible due to the deployment complexity and varying characteristics of devices and thus must instead be employed at the circuit-level and inferred through the incorporation of novel usage-based measurement and probabilistic level-based disaggregation algorithm. But the challenges in deploying non-intrusive load monitoring algorithm involve disaggregating individual device?s consumption from the aggregate power measurement, as well as modeling and incorporating the usage based prediction. Thus in this project we will focus on advanced machine learning and data analytics algorithms that capture the measurement based approach and circuit level NILM with the autonomous profiling and prediction logic to enable the deployment of flexible and fungible smart plug and the evolvability of future DR model in green building applications.

绿色建筑应用需要通过非侵入式负载监控 (NILM) 技术高效、细粒度地确定各种消费级设备的功耗模式，以便有效适应和渗透需求响应模型到消费级设备。在智能建筑能源管理的消费级设备中广泛采用这种需求响应策略的一个关键障碍是智能插头无法有效地确定、控制或推断多个设备串联的功耗模式。 在实践中，由于部署的复杂性和设备的不同特性，部署基于智能插头的 NILM 并获取大量设备的低级功耗测量通常很困难或不可能，因此必须在电路级采用并推断通过结合新颖的基于使用的测量和基于概率级别的分解算法。但部署非侵入式负载监控算法的挑战包括从总功率测量中分解单个设备的功耗，以及建模和合并基于使用情况的预测。因此，在这个项目中，我们将重点关注先进的机器学习和数据分析算法，这些算法通过自主分析和预测逻辑捕获基于测量的方法和电路级 NILM，从而实现灵活且可替换的智能插头的部署以及未来 DR 模型的可进化性。绿色建筑应用。