Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
The energy dashboard: improving the visibility of energy consumption at a campus-wide scale
Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
Occupancy based demand response HVAC control strategy
Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building
Building-level occupancy data to improve ARIMA-based electricity use forecasts
Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building
Granger causality analysis on IP traffic and circuit-level energy monitoring
Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building
Measuring building occupancy using existing network infrastructure
IGCC '11 Proceedings of the 2011 International Green Computing Conference and Workshops
Following the electrons: methods for power management in commercial buildings
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Accurate real-time occupant energy-footprinting in commercial buildings
BuildSys '12 Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
The user side of sustainability: Modeling behavior and energy usage in the home
Pervasive and Mobile Computing
Strip, bind, and search: a method for identifying abnormal energy consumption in buildings
Proceedings of the 12th international conference on Information processing in sensor networks
Exploiting Generalized Additive Models for Diagnosing Abnormal Energy Use in Buildings
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
Watts in the basket?: Energy Analysis of a Retail Chain
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
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Commercial buildings are significant consumers of electricity. In this paper, we collect and analyze six weeks of data from 39 power meters in three buildings of a campus of a large company. We use an unsupervised anomaly detection technique based on a low-dimensional embedding to identify power saving opportunities. Further, to better manage resources such as lighting and HVAC, we develop occupancy models based on readily available port-level network logs. We propose a semi-supervised approach that combines hidden Markov models (HMM) with standard classifiers such as naive Bayes and support vector machines (SVM). This two step approach simplifies the occupancy model while achieving good accuracy. The experimental results over ten office cubicles show that the maximum error is less than 15% with an average error of 9.3%. We demonstrate that using our occupancy models, we can potentially reduce the lighting load on one floor (about 45 kW) by about 9.5%.