Modeling Occupancy Behavior for Energy Efficiency and Occupants Comfort Management in Intelligent Buildings

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@InProceedings{Yu:2010:ICMLA,

• author = "Tina Yu",
• title = "Modeling Occupancy Behavior for Energy Efficiency and Occupants Comfort Management in Intelligent Buildings",
• booktitle = "Ninth International Conference on Machine Learning and Applications (ICMLA 2010)",
• year = "2010",
• month = "12-14 " # dec,
• pages = "726--731",
• address = "Washington, DC, USA",
• isbn13 = "978-1-4244-9211-4",
• keywords = "genetic algorithms, genetic programming, energy efficiency, intelligent buildings, motion sensor data, occupancy behaviour modelling, occupants comfort management, building management systems, energy conservation",
• URL = "http://www.cs.mun.ca/~tinayu/Publications_files/PID1505499.pdf",
• DOI = "doi:10.1109/ICMLA.2010.111",
• abstract = "We applied genetic programming algorithm to learn the behaviour of an occupant in single person office based on motion sensor data. The learnt rules predict the presence and absence of the occupant with 80percent-83percent accuracy on testing data from 5 different offices. The rules indicate that the following variables may influence occupancy behaviour: 1) the day of week, 2) the time of day, 3) the length of time the occupant spent in the previous state, 4) the length of time the occupant spent in the state prior to the previous state, 5) the length of time the occupant has been in the office since the first arrival of the day. We evaluate the rules with various statistics, which confirm some of the previous findings by other researchers. We also provide new insights about occupancy behaviour of these offices that have not been reported previously.",
• notes = "Dept. of Comput. Sci., Memorial Univ. of Newfoundland, St. John's, NL, Canada. Also known as \cite{5708933}",

}

Genetic Programming entries for Tina Yu

Citations