Building energy management using learning-from-signals

• Published in: 2012 Future of Instrumentation International Workshop (FIIW) Proceedings pp. 1 - 4
• Main Authors: Moore, M. R., Buckner, M. A., Young, M. A., Albright, A. P., Bobrek, M., Haynes, H. D., Wetherington, G. Randall
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2012
• Subjects: Buildings; Data models; Electricity; energy efficiency; learning-from-signals; machine learning; Monitoring; power consumption; Power demand; signal classification; signal mining; Transforms; US Department of Defense
• ISBN: 1467324833; 9781467324830
• DOI: 10.1109/FIIW.2012.6378351

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.