Title :
Building energy management using learning-from-signals
Author :
Moore, M.R. ; Buckner, Mark A. ; Young, Marcus A. ; Albright, A.P. ; Bobrek, M. ; Haynes, H.D. ; Wetherington, G. Randall
Author_Institution :
Commun. & Intell. Syst. Group, Oak Ridge Nat. Lab., Oak Ridge, TN, USA
Abstract :
ORNL recently applied its “learning-from-signals” (LFS) techniques to evaluating and improving the energy efficiency of buildings at military installations. LFS is a term coined by ORNL to describe the machine learning algorithms that it has developed for mining, processing, and classifying signals either purposefully or inadvertently being picked up from infrastructure or individual devices. For this particular application, ORNL provided technical support to the Defense Advanced Research Projects Agency (DARPA) Service Chiefs Program for disaggregating electrical power consumption at the device level in a military residential dormitory at Fort Meyer in Washington, DC. The ORNL researchers showed that patterns of device utilization could be monitored on a building´s power infrastructure. These devices included cooling/heating water pumps, lighting, washers, dryers, refrigerators, and stoves. This paper discusses the process and initial results of the research effort, as well as the path forward for similar industrial, commercial, and government undertakings.
Keywords :
building management systems; cooling; energy conservation; learning (artificial intelligence); lighting; military computing; power engineering computing; pumps; refrigerators; signal classification; space heating; DARPA Service Chief Program; DC; Defense Advanced Research Projects Agency; Fort Meyer; LFS technique; ORNL researchers; Washington; building energy management; building power infrastructure; cooling-heating water pumps; device level; device utilization; dryers; electrical power consumption; energy efficiency; learning-from-signals; lighting; machine learning algorithm; military installations; military residential dormitory; refrigerators; signal classification; signal mining; signal processing; stoves; washers; Buildings; Data models; Electricity; Monitoring; Power demand; Transforms; US Department of Defense; energy efficiency; learning-from-signals; machine learning; power consumption; signal classification; signal mining;
Conference_Titel :
Future of Instrumentation International Workshop (FIIW), 2012
Conference_Location :
Gatlinburg, TN
Print_ISBN :
978-1-4673-2483-0
DOI :
10.1109/FIIW.2012.6378351