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Title: Occupancy schedules learning process through a data mining framework

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 minute 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. Furthermore, 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 energymore » use in office buildings.« less
 [1] ;  [2]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Polytechnic of Turin, Torino (Italy)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
OSTI Identifier:
Report Number(s):
Journal ID: ISSN 0378-7788; ir:180204
Accepted Manuscript
Journal Name:
Energy and Buildings
Additional Journal Information:
Journal Volume: 88; Journal Issue: C; Journal ID: ISSN 0378-7788
Research Org:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Org:
Country of Publication:
United States
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION occupant behavior; data mining; occupancy schedule; behavioral pattern; office building; building simulation