An Interpretable Modeling Method for Occupancy in Public Buildings Based on Typical Occupancy Data

Occupancy, defined as the count of occupants, plays an important role in building design and operation stages. Obtaining reliable occupancy data for public buildings remains a challenging problem due to the lack of available on-site data. With the development of information technologies, the widespread use of smartphones and social networks provides a source for collecting building occupancy data. In this paper, we collect occupancy data of 56 public buildings from social networks. Based on this database, an interpretable occupancy model is proposed, incorporating the effects of trend, day types, months, meteorological parameters, and special events, such as the COVID-19 period, discount days, etc. The modeling process includes following four steps: (1) extracting typical occupancy data (TOD), (2) extracting key factors through the CatBoost model and SHAP method, (3) model fitting, and (4) model transfer application. The proposed method quantifies the influence of different factors on occupancy and can be applied to simulate occupancy in public buildings without on-site data. Its performance is evaluated through a case study on four public buildings in this paper.