TY - GEN
T1 - Building energy management using learning-from-signals
AU - Moore, Michael R.
AU - Buckner, Mark A.
AU - Young, Marcus A.
AU - Albright, Austin P.
AU - Bobrek, Miljko
AU - Haynes, Howard D.
AU - Wetherington, G. Randall
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - energy efficiency
KW - learning-from-signals
KW - machine learning
KW - power consumption
KW - signal classification
KW - signal mining
UR - http://www.scopus.com/inward/record.url?scp=84872026434&partnerID=8YFLogxK
U2 - 10.1109/FIIW.2012.6378351
DO - 10.1109/FIIW.2012.6378351
M3 - Conference contribution
AN - SCOPUS:84872026434
SN - 9781467324823
T3 - FIIW 2012 - 2012 Future of Instrumentation International Workshop Proceedings
SP - 17
EP - 20
BT - FIIW 2012 - 2012 Future of Instrumentation International Workshop Proceedings
PB - IEEE Computer Society
T2 - 2012 Future of Instrumentation International Workshop, FIIW 2012
Y2 - 8 October 2012 through 9 October 2012
ER -
