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