Artificial Intelligence for Efficient Thermal Comfort Systems: Requirements, Current Applications and Future Directions

Ali Ghahramani,David Lehrer,Parson Galicia,Yogesh Pandit,Zhe Wang,Zubin Varghese

doi:10.3389/fbuil.2020.00049

Abstract

Author(s): Ghahramani, Ali; Galicia, Parson; Lehrer, David; Varghese, Zubin; Wang, Zhe; Pandit, Yogesh | Abstract: In buildings, one or a combination of systems (e.g., central HVAC system, ceiling fan, desk fan, personal heater, and foot warmer) are often responsible for providing thermal comfort to the occupants. While thermal comfort has been shown to differ from person to person and vary over time, these systems are often operated based on preﬁxed setpoints and schedule of operations or at the request/routine of each individual. This leads to occupants’ discomfort and energy wastes. To enable the improvements in both comfort and energy efﬁciency autonomously, in this paper, we describe the necessity of an integrated system of sensors (e.g., wearable sensors/infrared sensors), infrastructure for enabling system interoperability, learning and control algorithms, and actuators (e.g., HVAC system setpoints, ceiling fans) to work under a governing central intelligent system. To assist readers with little to no exposure to artiﬁcial intelligence (AI), we describe the fundamentals of an intelligent entity (rational agent) and components of its problem-solving process (i.e., search algorithms, logic inference, and machine learning) and provide examples from the literature. We then discuss the current application of intelligent personal thermal comfort systems in buildings based on a comprehensive review of the literature. We ﬁnally describe future directions for enabling application of fully automated systems to provide comfort in an efﬁcient manner. It is apparent that improvements in all aspects of an intelligent system are be needed to better ascertain the correct combination of systems to activate and for how long to increase the overall efﬁciency of the system and improve comfort.

Highlights

Indoor environments have become humans’ dominant habitat as we spend more than 90% of our time indoors (Klepeis et al, 2001)
We focus on three main categories of machine learning: supervised learning (SL), reinforcement learning, and unsupervised learning (UL)
We discussed the current application of intelligent personal thermal comfort systems in buildings by describing comfort related disjointed and connected systems

Summary

Introduction

Indoor environments have become humans’ dominant habitat as we spend more than 90% of our time indoors (Klepeis et al, 2001). This study brings attention to the needs for further development of operational devices found in occupied spaces to better provide building occupants more comfortable thermal environments.

Results

Conclusion