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Abstract
This study introduced a new methodology to estimate how often regularly-inhabited buildings are vacant and the electricity consumed during these times. In empty buildings, energy use can be greatly reduced through aggressive conservation measures, though few methods are currently available to easily estimate vacancy. The presented method uses aggregated Wi-Fi access point connection data that building operators can incorporate at almost no cost and apply to an entire portfolio of buildings.
The new vacancy inference approach was applied to 24 University of California, Davis (UCD) campus buildings using six months of Wi-Fi and electricity data. During this period of analysis, the buildings were, on average, vacant 29% of the time with 24% of total electricity consumed during periods of vacancy. A newly proposed Vacant Building Energy Metric (VBEM) integrates the Wi-Fi inference results with an existing electricity metric to rank buildings according to their energy savings opportunity during vacancy.
Miscellaneous energy loads (MELs) are a growing portion of building energy use, but limited solutions exist to address them. As an example of a MELs application of vacancy intelligence, this study also showcased classroom audio-video (AV) equipment. Applying the new vacancy inference method with AV power monitoring data revealed that AV equipment in UCD General Assignment Classrooms accounts for approximately 270 MWh per year, with 80 MWh taking place at times when buildings are empty.