Modeling and Control of Ventilation Strategies in University Rooms for Enhanced Energy Efficiency

Internet of Things (IoT) devices, Home and Building Automation solutions, and Artificial Intelligence (AI)-based control strategies are among the most attractive enabling technologies to manage Heating, Ventilation, and Air Conditioning (HVAC) appliances in buildings, to decrease energy consumption while retaining – and sometimes even improving – the comfort of the people working or living in the indoor environment. We focus on ventilation systems that, because of the Covid-19 pandemic, have been recently converted to exchange air exclusively with the outside, rather than partially (or totally) working through air recirculation systems. While air exchange with the outside was particularly effective during the pandemic to mitigate infection risks, this is becoming particularly expensive in winter or summer due to increased costs for extra heating, or cooling, of the air from the outside. In this paper, we provide an accurate model of mechanical and natural ventilation systems and compare automatic control strategies that combine them to minimize energy consumption. Our case study throughout the manuscript consists of university rooms characterized by occupancy levels that vary significantly during the days, but with a predictable pattern that follows scheduled classes and lessons. As we shall demonstrate, significant energy savings can be achieved in a fully sensorized and automated environment without compromising the comfort of occupants, especially in winter, when an extensive utilization of mechanical ventilation systems is expensive, or when there is a small number of occupants. Our findings highlight the potential for smart control systems to enhance energy efficiency in modern university buildings.