02039nas a2200217   4500008003900000245007300039210006900112260001200181300001200193490000700205520135500212653002301567653002401590653001601614653002301630653002201653653002001675100001801695700001901713856008901732       2015                          d00aOccupancy Schedules Learning Process Through a Data Mining Framework0 aOccupancy Schedules Learning Process Through a Data Mining Frame  c02/2015  a395-4080 v883 a<p>Building occupancy is a paramount factor in building energy simulations. Specifically, lighting, plug loads, HVAC equipment utilization, fresh air requirements and internal heat gain or loss greatly depends on the level of occupancy within a building. Developing the appropriate methodologies to describe and reproduce the intricate network responsible for human-building interactions are needed. Extrapolation of patterns from big data streams is a powerful analysis technique which will allow for a better understanding of energy usage in buildings. A three-step data mining framework is applied to discover occupancy patterns in office spaces. First, a data set of 16 offices with 10&nbsp;min interval occupancy data, over a two year period is mined through a decision tree model which predicts the occupancy presence. Then a rule induction algorithm is used to learn a pruned set of rules on the results from the decision tree model. Finally, a cluster analysis is employed in order to obtain consistent patterns of occupancy schedules. The identified occupancy rules and schedules are representative as four archetypal working profiles that can be used as input to current building energy modeling programs, such as EnergyPlus or IDA-ICE, to investigate impact of occupant presence on design, operation and energy use in office buildings.</p>10aBehavioral Pattern10abuilding simulation10adata mining10aOccupancy schedule10aoccupant behavior10aOffice Building1 aD'Oca, Simona1 aHong, Tianzhen  uhttps://simulationresearch.lbl.gov/publications/occupancy-schedules-learning-process