Indoor environmental quality evaluation of smart/artificial intelligence techniques in buildings – a review

Ecole Nationale des Travaux Publics de l’Etat (ENTPE), Laboratoire de Tribologie et de Dynamique des Systèmes (LTDS), 3 Rue Maurice Audin, 69120, Vaulx-en-Velin, France

^* Corresponding author: joud.aljumaaaldakheel@entpe.fr

Abstract

The built environment sector is responsible for around one-third of the world's final energy consumption. Smart technologies play an essential role in strengthening existing regulations and facilitating energy efficiency targets. Smart Buildings allow the response to the external conditions of buildings including grid and climatic conditions, and internal building needs such as user requirements achieved through real-time monitoring and real-time interaction which are resembled the smart buildings concept. The optimal management of occupant comfort plays a crucial role in the built environment since the occupant's productivity and health are highly influenced by Indoor Environmental Quality. This work explores the application of real-time monitoring and interaction to achieve optimal Indoor Environmental Quality, occupant comfort and energy savings in relation to smart buildings and smart technologies. To better address and indoor air quality issues, ventilation needs to become smarter. It is crucial to understand first the Key Performance Indicators of evaluating smart ventilation. In parallel, Artificial Intelligence techniques such as machine and deep learning have been increasingly and successfully applied to develop solutions for the built environment. Thus, this paper provides a review on the existing Key Performance Indicators that allows smart ventilation in smart buildings. Then, it reviews the existing literature on the machine and deep learning methods and software for assessing the smart ventilation. Finally, it shows the most recent technologies for performing experimental evaluation on the main indicators for smart ventilation. This work is expected to highlight the selection of the most optimal ventilation metrics, proper indicators, machine learning and deep learning models and measurement technologies to achieve excellent Indoor Environmental Quality and energy efficiency levels.