Journal of the Architectural Institute of Korea (대한건축학회논문집)

Architectural Institute of Korea (대한건축학회)
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A Model for Classification of Occupant Behavior based on Building Environmental Data by Seasons

계절별 실내 환경 데이터 기반 재실자 행동 분류 모델 개발

  • Received : 2020.06.29
  • Accepted : 2020.10.23
  • Published : 2020.11.30
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Abstract

It is important to have detailed information on the number of occupants and their activities for appropriate building operation and control of HVAC systems. Indoor environment is affected by using thermal environmental devices, and the occupant's activities as well. Thus, this study focuses on the classification of occupant's activities using machine learning algorithms with indoor environmental data. We developed an occupant's status detection model by seasons(summer, winter, summer and winter) using classification algorithms. Data collection was performed in a Smart Living Testbed. This study categorized occupant's status into 7 activities; sleeping, resting, working, cooking, eating, exercising, or away. Two classification algorithms(KNN, Random Forest) were evaluated for the development of an occupant's behavior classification model. For Random Forest model using summer data, the accuracy of the occupant behavior detection model was 95.96% and for KNN, the accuracy was 94.75%. For models using winter data, the accuracy of Random Forest model was 98.91% and KNN was 98.90%. When we used summer and winter data together for the classification models, the accuracies of both models were 97.82% for Random Forest and 97.16% for KNN, respectively. However, cooking and rest showed lower accuracies compared to other activities.

Keywords

Acknowledgement

본 연구는 국토교통부 도시건축사업의 연구비지원(20AUDP-B099686-06)에 의해 수행되었습니다. 이 논문은 정부(교육과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(NRF-2018R1A2A2A05023124). 이 논문은 2019년도 정부(교육부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임(NRF-2019R1I1A1A01061960)

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