An opportunistic activity-sensing approach to save energy in office buildings

Publication Type Conference Paper
Authors Marija Milenkovic, Oliver Amft
Title An opportunistic activity-sensing approach to save energy in office buildings
Abstract In this work, we recognised office worker activities that are relevant for energy-related control of appliances and building systems using sensors that are commonly installed in new or refurbished office buildings. We considered desk-related activities and people count in office rooms, structured into desk- and room-cells. Recognition was performed using finite state machines (FSMs) and probabilistic layered hidden Markov models (LHMMs). We evaluated our approach in a real living-lab office, including three private and multi-person office rooms. As example devices, we used different ceiling-mounted PIR sensors based on the EnOcean platform and plug-in power meters. In at least five days of study data per office room, including reference sensor data and occupant annotations, we confirmed that activities can be recognised using these sensors. For computer and desk work, an overall recognition accuracy of 95% was achieved. People count was estimated at 87% and 78% for the best-performing two office rooms. We furthermore present building simulation results that compare different control strategies. Compared to modern BEMS, our results show that 21.9% and 19.5% of electrical energy can be saved for controls based on recognised desk activity and estimated people count, respectively. These results confirm the relevance of building energy management based on activity sensing.
Date 2013
Proceedings Title eEnergy 2013: Proceedings of the Fourth International Conference on Future Energy Systems
Publisher ACM
Pages 247–258
DOI 10.1145/2487166.2487194
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Friedrich-Alexander-Universität Erlangen-Nürnberg