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

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.

Causal AI-Powered Event Interpretation: A Cause-and-Effect Discovery for Indoor Thermal Comfort Measurements

“…Thermal comfort considers how to control dynamic factors that influence the occupants' thermal sensation in an indoor environment. De Dear and Brager [10] introduced the occupant factors (e.g., clothing and activities), environment factors (e.g., temperature and humidity), and air condition factors (e.g., air temperature and airflow speed) that cause the thermal comfort events to change over time [11]. We employ this background knowledge to review related works for interpreting indoor environments.…”

Energy Management Systems and Strategies in Buildings Sector: A Scoping Review

“…In this setting, BEMSs face difficulties to manage related energy equipment easily to achieve high energy saving. Additionally, due to the need to optimally and effectively perform BEMS-related functions for smart buildings utilizing big data collected from a number of smart devices, the design of such a BEMS needs to carefully consider a lot of criteria and conditions [153].…”

Artificial intelligence as an agent to transform research paradigms in building science and technology

“…15 AI logic is designed to produce logical reasoning and is programmed to make rational decisions and achieve a goal that can be integrated with building energy management systems (BEMS) for effective environmental control. 16 A certain amount of energy is expended in order to provide adequate comfort levels to the building's inhabitants. It is a significant challenge to strike a balance between reducing building energy consumption and ensuring acceptable comfort levels.…”