Authors: Zheyu Zhang, Jianfeng He, Avinash Kumar and Saifur Rahman
Conference: 2024 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT)
DOI: https://doi.org/10.1109/ISGT59692.2024.10454176
Abstract: Building energy management - as a tool to effect day-to-day energy savings - is influenced by space occupancy levels. The use of video images to estimate occupancy levels has privacy concerns and is cost ineffective. In this paper, a Deep Learning (DL) based approach is proposed to estimate the number of people in a given space using environmental sensor data. Five environmental factors are considered to train and test the proposed model. The input data are pre-processed with Zscore normalization for better performance of the model. Further, the proposed method is compared with Long Short Term Memory (LSTM) with higher F1 score. In addition, to improve the estimation accuracy of space occupancy, one hot encoding is done for output data. The proposed model estimates the number of students in classrooms with high accuracy in a cost-effective manner while maintaining privacy. The application of the proposed approach is to improve energy efficiency by utilising the estimated headcount information.