| Title |
A Model for Classification of Occupant Behavior based on Building Environmental Data by Seasons |
| Authors |
이예린(Ye Rin Lee) ; 윤영란(Young Ran Yoon) ; 문현준(Hyeun Jun Moon) |
| DOI |
https://doi.org/10.5659/JAIK.2020.36.11.239 |
| Keywords |
Occupant status; Occupant activity; Environmental data; Classification; Machine Learning |
| 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. |