Occupancy Estimation in Academic Laboratory: A CO2-Based Algorithm Incorporating Temporal Features for 1–16 Occupants

Private, non-intrusive presence detection methods contribute to various applications, from occupancy monitoring to energy optimization and security. This study presents a deep learning approach for predicting occupancy patterns using CO2 sensor data and temporal features, derived from a year-long dataset (18 September 2023–21 November 2024) collected via the Smart Indoor Air Quality Monitor. We created a dataset of 19,189 samples of CO2 levels (0–5000 ppm) with timestamps. A sequential neural network with three fully connected layers was implemented in TensorFlow. The developed model demonstrated the feasibility of predicting occupancy based on CO2 data and temporal features with an accuracy of 0.97 and an F1-score of 0.92. Model visualization was performed using heatmaps. Its advantages include low computational requirements, cost-effective sensors, an IoT-enabled interface, and scalability. However, the study is limited to a university laboratory with a capacity of 1–16 occupants, which may impact its generalizability to other settings. These findings highlight the utility of CO2 levels and temporal features for occupancy estimation in laboratory conditions and contribute a unique, long-term multimodal dataset to the research community.