Field Trial of a Low-Cost, Distributed Plug Load Monitoring System

Auchter, B. ; Cautley, D. ; Ahl, D. ; Earle, L. ; Jin, X.

关键词: RESIDENTIAL;    RESIDENTIAL BUILDINGS;    NREL;    BUILDING AMERICA;    PLUG LOAD;    MELS;    AHEM;    MONITORING;    WIRELESS;    DISTRIBUTED;    NILM;    ENERGY METER​;    Buildings;   

DOI  :  10.2172/1126834
RP-ID  :  NREL/TP-5500-61257
PID  :  OSTI ID: 1126834

美国|英语

来源: SciTech Connect

【 摘 要 】

Researchers have struggled to inventory and characterize the energy use profiles of the ever-growing category of so-called miscellaneous electric loads (MELs) because plug-load monitoring is cost-prohibitive to the researcher and intrusive to the homeowner. However, these data represent a crucial missing link to our understanding of how homes use energy, and we cannot control what we do not understand. Detailed energy use profiles would enable the nascent automated home energy management (AHEM) industry to develop effective control algorithms that target consumer electronics and other plug loads. If utility and other efficiency programs are to incent AHEM devices, they need large-scale datasets that provide statistically meaningful justification of their investments by quantifying the aggregate energy savings achievable. To address this need, we have investigated a variety of plug-load measuring devices available commercially and tested them in the laboratory to identify the most promising candidates for field applications. The scope of this report centers around the lessons learned from a field validation of one proof-of-concept system, called Smartenit (formerly SimpleHomeNet). The system was evaluated based on the rate of successful data queries, reliability over a period of days to weeks, and accuracy. This system offers good overall performance when deployed with up to ten end nodes in a residential environment, although deployment with more nodes and in a commercial environment is much less robust. We conclude that the current system is useful in selected field research projects, with the recommendation that system behavior is observed over time.