Smart Occupancy-Driven Control for Ventilation Systems in Buildings with Privacy Concerns

Nowadays occupancy monitoring solutions are an intrinsic component of modern buildings to track and optimize the utilization of spaces and resources. Such solutions form a ground to the decision support of buildings management and rely on a large set of sensors that are commonly part of the LHVAC (lighting, heating, ventilation, and air-condition) systems. However, with the increasing privacy concerns it is becoming challenging to deploy seeing/hearing sensors. In this paper we propose an accurate occupancy estimator using machine learning (ML) and blind CO2$$\text {CO}_{2}$$ sensors to assess the actual occupancy state and predict future states. We also design a smart control system, relying on the occupancy module, to actuate the ventilation system in an energy efficient manner while maintaining the safety properties dictated by CO2$$\text {CO}_{2}$$ standards. In order to improve the prediction accuracy of ML models, we use the DBSCAN outlier to filter the input data and Z-Score to normalize such data. The system is fully implemented and tested on a data set of 8000 samples. The experimental results show that the prediction accuracy of our system is 96.6% with a considerable impact of the DBSCAN outlier. Furthermore, our smart control system enables an improvement of the energy performance and provides an efficient peak shaving to the energy consumption of the ventilation system.