Strip, bind, and search: a method for identifying abnormal energy consumption in buildings

Authors:
Romain Fontugne;Jorge Ortiz;Nicolas Tremblay;Pierre Borgnat;Patrick Flandrin;Kensuke Fukuda;David Culler;Hiroshi Esaki
Affiliations:
The University of Tokyo, Tokyo, Japan;University of California, Berkeley, Berkeley, CA, USA;CNRS, Ecole Normale Supérieure de Lyon, Lyon, France;CNRS, Ecole Normale Supérieure de Lyon, Lyon, France;CNRS, Ecole Normale Supérieure de Lyon, Lyon, France;National Institute of Informatics, Tokyo, Japan;University of California, Berkeley, Berkeley, CA, USA;The University of Tokyo, Tokyo, Japan
Venue:
Proceedings of the 12th international conference on Information processing in sensor networks
Year:
2013

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Abstract

A typical large building contains thousands of sensors, monitoring the HVAC system, lighting, and other operational sub-systems. With the increased push for operational efficiency, operators are relying more on historical data processing to uncover opportunities for energy-savings. However, they are overwhelmed with the deluge of data and seek more efficient ways to identify potential problems. In this paper, we present a new approach called the Strip, Bind and Search (SBS); a method for uncovering abnormal equipment behavior and in-concert usage patterns. SBS uncovers relationships between devices and constructs a model for their usage pattern relative to other devices. It then flags deviations from the model. We run SBS on a set of building sensor traces; each containing hundred sensors reporting data flows over 18 weeks from two separate buildings with fundamentally different infrastructures. We demonstrate that, in many cases, SBS uncovers misbehavior corresponding to inefficient device usage that leads to energy waste. The average waste uncovered is as high as 2500~kWh per device.