Occupancy data analytics and prediction: A case study

Xin Liang, Tianzhen Hong, Qiping Shen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

59 Citations (Scopus)

Abstract

Occupants are a critical impact factor of building energy consumption. Numerous previous studies emphasized the role of occupants and investigated the interactions between occupants and buildings. However, a fundamental problem, how to learn occupancy patterns and predict occupancy schedule, has not been well addressed due to highly stochastic activities of occupants and insufficient data. This study proposes a data mining based approach for occupancy schedule learning and prediction in office buildings. The proposed approach first recognizes the patterns of occupant presence by cluster analysis, then learns the schedule rules by decision tree, and finally predicts the occupancy schedules based on the inducted rules. A case study was conducted in an office building in Philadelphia, U.S. Based on one-year observed data, the validation results indicate that the proposed approach significantly improves the accuracy of occupancy schedule prediction. The proposed approach only requires simple input data (i.e., the time series data of occupant number entering and exiting a building), which is available in most office buildings. Therefore, this approach is practical to facilitate occupancy schedule prediction, building energy simulation and facility operation.
Original languageEnglish
Pages (from-to)179-192
Number of pages14
JournalBuilding and Environment
Volume102
DOIs
Publication statusPublished - 1 Jun 2016

Keywords

  • Data mining
  • Machine learning
  • Occupancy prediction
  • Occupant presence

ASJC Scopus subject areas

  • Environmental Engineering
  • Civil and Structural Engineering
  • Geography, Planning and Development
  • Building and Construction

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