Application of Inductive Monitoring System to Plug Load Anomaly Detection

Shared by SCOTT POLL, updated on Dec 12, 2013


Author(s) :
C. Teubert, S. Poll

NASA Ames Research Center’s Sustainability Base is a new
50,000 sq. ft. LEED Platinum office building. Plug loads
are expected to account for a significant portion of the overall
energy consumption. This is because building design choices
have resulted in greatly reduced energy demand from Heating,
Ventilation, and Air Conditioning (HVAC) and lighting
systems, which are major contributors to energy consumption
in traditional buildings. In anticipation of the importance of
plug loads in Sustainability Base, a pilot study was conducted
to collect data from a variety of plug loads. A number of cases
of anomalous or unhealthy behavior were observed including
schedule-based rule failures, time-to-standby errors, changed
loads, and inter-channel anomalies. These issues prevent effective
plug load management; therefore, they are important
to promptly identify and correct. The Inductive Monitoring
System (IMS) data mining algorithm was chosen to identify
errors. This paper details how an automated data analysis program
was created, tested and implemented using IMS. This
program will be applied to Sustainability Base to maintain
effective plug load management system performance, identify
malfunctioning equipment, and reduce building energy

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