Occupancy Modeling and Prediction for Building Energy Management

Varick L. Erickson, Miguel A. Carreira-Perpinan, Alberto E. Cerpa


Heating, cooling and ventilation accounts for 35% energy usage in the United States. Currently, most modern buildings still condition rooms assuming maximum occupancy rather than actual usage. As a result, rooms are often over-conditioned needlessly. Thus, in order to achieve efficient conditioning, we require knowledge of occupancy. This paper shows how real time occupancy data from a wireless sensor network can be used to create occupancy models, which in turn can be integrated into building conditioning system for usage based demand control conditioning strategies. Using strategies based on sensor network occupancy model predictions, we show that it is possible to achieve 42% annual energy savings while still maintaining American Society of Heating, Refrigerating and Air-Conditioning (ASHRAE) comfort standards.




Varick L. Erickson, Miguel A. Carreira-Perpinan, Alberto E. Cerpa, "Occupancy Modeling and Prediction for Building Energy Management," ACM Transactions on Sensor Networks (TOSN), 10, (3), pp. 29 pages, August, 2014.


  author =       "Varick L. Erickson and Miguel A. Carreira-Perpinan and
                 Alberto E. Cerpa",
  title =        "Occupancy Modeling and Prediction for Building Energy
  journal =      "ACM Transactions on Sensor Networks (TOSN)",
  volume =       "10",
  number =       "3",
  month =        aug,
  pages =        "29 pages",
  note =         "Extended version of the IPSN 2011 conference paper",
  year =         "2014",
  URL =          "http://www.andes.ucmerced.edu/papers/Erickson14a.pdf",


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