A framework for occupancy prediction based on image information fusion and machine learning

It is of great energy-saving significance to provide accurate occupancy schedules for building energy simulation. Inaccurate occupancy information can lead to biased results. However, the reference occupancy schedules provided by the standard documents are fixed and differ significantly from the actual occupancy fluctuations. Moreover, the large amount of accurate occupancy information is difficult and expensive to obtain, limiting the development of occupancy fluctuation pattern exploration and occupancy prediction. In this paper, a framework is proposed to provide accurate and flexible occupancy schedules based on occupancy-related variables and occupancy information extracted from surveillance videos through Convolutional Neural Network-based density estimation methods. Machine learning algorithms are used in the framework to predict occupancy owing to their powerful predictive capabilities. A case study based on educational buildings is conducted to examine the performance of the framework and to compare its predicted occupancy schedules with those provided by national standard documents. The average recognition accuracy of the proposed framework for different crowd densities is as high as 95.67%, and the mean absolute percentage error for the prediction of the occupancy by the proposed framework is only 16.88%. The average deviation degree between the predicted occupancy rate schedules and the actual occupancy rate schedules is reduced from 676.08% to 23.19%. The average deviation degree of building energy simulation results caused by the occupancy rate schedule is reduced from 42.56% to 0.93%. These results provide conclusive evidence that the proposed framework can provide realistic and flexible occupancy schedules to improve the accuracy of building energy simulation.