An AI-Based Ventilation KPI Using Embedded IoT Devices

Abstract

The air ventilation of enclosed premises has a direct impact on the occupants' well-being. If not properly regulated, the air ventilation can originate a multitude of diseases and pathologies. The present study proposes a new KPI (ventilation KPI) adapted to Smart Cities. It is especially designed for academic environments (Smart Universities) in which community members spend a long time gathered in classrooms, seminars, laboratories, etc. The ventilation KPI (or KPIv) was designed to support decision making and is based on the estimation of the number of occupants of an enclosed space and the accumulation of existing CO2. Two AI techniques are proposed to perform these estimations, specifically, two regressive neural networks. The resulting models, together with the KPI, were implemented through the development of value-added services for the University of Alicante's Smart University platform. The network models were designed to be embedded within the built IoT device prototypes. These prototypes are small and inexpensive. They act as intelligent sensors and are connected via a low consumption and emission network (LoRa). The case study showed that it is possible to take advantage of the preexisting services and resources of these platforms and to validate the KPIv.