Proceedings of the First ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
Following the electrons: methods for power management in commercial buildings
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
COPOLAN: non-invasive occupancy profiling for preliminary assessment of HVAC fixed timing strategies
Proceedings of the Third ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
Enabling building energy auditing using adapted occupancy models
Proceedings of the Third ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
Towards an understanding of campus-scale power consumption
Proceedings of the Third ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
Forecasting building occupancy using sensor network data
Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Non-Intrusive Occupancy Monitoring using Smart Meters
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
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The energy use of an office building is likely to correlate with the number of occupants, and thus knowing occupancy levels should improve energy use forecasts. To gather data related to total building occupancy, wireless sensors were installed in a three-storey building in eastern Ontario, Canada comprising laboratories and 81 individual work spaces. Contact closure sensors were placed on various doors, PIR motion sensors were placed in the main corridor on each floor, and a carbon-dioxide sensor was positioned in a circulation area. In addition, we collected data on the number of people who had logged in to the network on each day, network activity, electrical energy use (total building, and chilling plant only), and outdoor temperature. We developed an ARIMAX model to forecast the power demand of the building in which a measure of building occupancy was a significant independent variable and increased the model accuracy. The results are promising, and suggest that further work on a larger and more typical office building would be beneficial. If building operators have a tool that can accurately forecast the energy use of their building several hours ahead they can better respond to utility price signals, and play a fuller role in the coming Smart Grid.