Application of Inductive Monitoring System to Plug Load Anomaly Detection



Published Sep 23, 2012
Christopher Teubert Scott 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 consumption.

How to Cite

Teubert, C. ., & Poll, S. . (2012). Application of Inductive Monitoring System to Plug Load Anomaly Detection. Annual Conference of the PHM Society, 4(1).
Abstract 219 | PDF Downloads 100



anomaly detection, data mining, plug loads, miscellaneous electrical loads

Bradley, P. S., & Fayyad, U. M. (1998). Refining initial points for k-means clustering. In Proceedings of the fifteenth international conference on machine learning (pp. 91–99).

Iverson, D. L. (2004). Inductive system health monitoring. In Proceedings of the 2004 international conference on artificial intelligence. CSREA Press.

Iverson, D. L., Spirkovska, L., & Schwabacher, M. (2010). General purpose data-driven online system health monitoring with applications to space operations. In Proceedings of the fifty-third annual isa powid symposium. Research Triangle Park, NC: International Society of Automation.

Kaneda, D., Jacobson, B., & Rumsey, P. (2010). Plug load re- duction: The next big hurdle for net zero energy building design. In Proceedings of 2010 aceee summer study on energy efficiency in buildings. Washington, D.C.: American Council for an Energy Efficient Economy.

Kantardzic, M. (2011). Data mining: Concepts, models, methods, and algorithms. Hoboken, New Jersey: John Wiley and Sons, Inc.
Lobato, C., Pless, S., Sheppy, M., & Torcellini, P. (2011).

Reducing plug and process loads for a large scale, low energy office building: Nrel’s research support facility (Tech. Rep. No. NREL/CP-5500-49002). National Renewable Energy Laboratory.

Poll, S., & Teubert, C. (2012). Pilot study of a plug load management system: Preparing for sustainability base. In Proceedings of 2012 IEEE green technologies conference. Institute of Electrical and Electronic Engineers, Inc.
Technical Research Papers