Short term predictions of occupancy in commercial buildings—Performance analysis for stochastic models and machine learning approaches

Abstract Real-time occupancy predictions are essential components for the smart buildings in the imminent future. The occupancy information, such as the presence states and the occupants’ number, allows a robust control of the indoor environment to enhance the building energy performances. With many current studies focusing on the commercial building occupancy, most researchers modeled either the occupancy presence or the occupants’ number without evaluating the model potentials on both of them. This study focuses on 1) providing a unique data set containing the occupancy for the offices located in the U.S with difference pattern varieties, 2) proposing two methods, then comparing them with four existing methods, and 3) both presence of occupancy and occupancy number are predicted and tested using the approaches proposed in this study. In detail, the paper develops a new moving-window inhomogeneous Markov model based on change point analysis. A hierarchical probability sampling model is modified based on existed models. They are additional compared to well-known models from previous researchers. The study further explores and evaluates the predictive power of the models by various temporal scenarios, including 15-min ahead, 30-min ahead, 1-h ahead, and 24-h ahead forecasts. The final results show that the proposed Markov model outperforms the other methods with a max 22% difference in terms of presence forecasts for 15-min, 30 min and 1-h ahead. The proposed Markov model also outperforms other models in occupancy number prediction for all forecast windows with 0.34 RMSE and 0.23 MAE error respectively. However, there is not much performance difference between models for 24-h ahead predictions of occupancy presence forecast.