Following the electrons: methods for power management in commercial buildings

Authors:
Gowtham Bellala;Manish Marwah;Martin Arlitt;Geoff Lyon;Cullen Bash
Affiliations:
HP Labs, Palo Alto, CA, USA;HP Labs, Palo Alto, CA, USA;HP Labs, Palo Alto, CA, USA;HP Labs, Palo Alto, CA, USA;HP Labs, Palo Alto, CA, USA
Venue:
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2012

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Abstract

Commercial buildings are significant consumers of electricity. The first step towards better energy management in commercial buildings is monitoring consumption. However, instrumenting every electrical panel in a large commercial building is expensive and wasteful. In this paper, we propose a greedy meter (sensor) placement algorithm based on maximization of information gained, subject to a cost constraint. The algorithm provides a near-optimal solution guarantee. Furthermore, to identify power saving opportunities, we use an unsupervised anomaly detection technique based on a low-dimensional embedding. Further, to better manage resources such as lighting and HVAC, we propose a semi-supervised approach combining hidden Markov models (HMM) and a standard classifier to model occupancy based on readily available port-level network statistics.