| Abstract | Wireless sensors were installed in a three-storey building in eastern Ontario comprising laboratories and 81 individual work spaces. Contact closure sensors were placed on exterior doors, internal doors, and on the refrigerator door in the main break room, 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 (bit transfer rates), and electrical energy use (total building, and chilling plant only). Data were recorded over the Summer, 2009. The data streams were clearly responsive to building occupancy at the whole building level (e.g. evenings, weekends, public holidays) and locally (e.g. doors near heavily-occupied meeting rooms). Further, we developed an ARIMAX model to forecast the power demand of the building in which an explicit measure of building occupancy level was a significant independent variable and increased the accuracy of the model. 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. |
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