Thermal comfort in buildings has traditionally been measured solely by temperature. While other methods such as Predicted Mean Vote (PMV) are available for measuring thermal comfort, the parameters required for an accurate value are overly complicated to obtain and require a great deal of sensory input. In this talk, we propose to bypass overly cumbersome or simplistic measures thermal comfort by bringing humans in the loop. By using humans as sensors, we can accurately adjust temperatures to improve occupant comfort. We show that occupants are more comfortable with a system that continually adjusts to thermal preference than a system that attempts to predict user comfort based on environmental factors. In addition, we also show that such a system is able to save 10.1% energy while improving the quality of service.
Alberto Cerpa is an Assistant Professor and Founding Faculty at UC Merced. He received the Ph.D. in CS from UCLA in 2005, the M.Sc. degree in CS from USC in 2000, the M.Sc. in EE from USC in 1998, and the Engineer degree in EE from the Buenos Aires Institute of Technology, Argentina in 1995. His interests lie broadly in the computer networking and distributed systems areas. His recent focus has been on systems research in wireless sensor networks, with emphasis on network self-configuration, radio channel measurement and characterization, programming models, and development of wireless testbeds. Dr. Cerpa applies sensor network technology to a wide range of application domains, including building energy management, solar radiation mapping and control for solar energy generation, exercise physiology monitoring and modeling, among others.
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