An IoT framework for the assessment of indoor conditions and estimation of occupancy rates: results from a real case study

Abstract

In recent years, energy-savings policies have affected many aspects of everyday life. Considering a typical building, the heating, ventilation, and air conditioning (HVAC) system is the most energy-consuming system. This consideration is especially true for large public-access buildings, such as schools, and public administrations. In these cases, the energy saving of buildings depends on the capability to optimise the behaviour of the HVAC. Typically, the HVAC control system is based on static models of the building, which consider the average occupancy rate of each of the rooms. On the contrary, in this research work, a cognitive system based on an occupancy rate model that is able to take into consideration user habits and indoor air quality (IAQ) provided by IoT sensors is considered for the control of HVAC systems. This approach has been applied to the eLUX lab building of the University of Brescia, Italy. Data provided by IoT IAQ sensors (temperature, relative humidity, CO₂) is used to refine the results of the occupancy rates for the models of the rooms of this building. The experimental results show, as in 22.15 % of the samples, the CO₂ concentration exceeded the 1,000 ppm threshold of the perception of fresh air and good conditions.