Cyber-Enabled Optimization of HVAC System Control in Open Space of Office Building

Abstract

Thermal comfort is crucial to well-being and work productivity. Human thermal comfort is mainly controlled by HVAC (heating, ventilation, air conditioning) systems in buildings. However, the control metrics and measurements of thermal comfort in HVAC systems are often oversimplified using limited parameters and fail to accurately control thermal comfort in indoor climates. Traditional comfort models also lack the ability to adapt to individual demands and sensations. This research developed a data-driven thermal comfort model to improve the overall thermal comfort of occupants in office buildings. An architecture based on cyber-physical system (CPS) is used to achieve these goals. A building simulation model is built to simulate multiple occupants’ behaviors in an open-space office building. Results suggest that a hybrid model can accurately predict occupants’ thermal comfort level with reasonable computing time. In addition, this model can improve occupants’ thermal comfort by 43.41% to 69.93%, while energy consumption remains the same or is slightly reduced (1.01% to 3.63%). This strategy can potentially be implemented in real-world building automation systems with appropriate sensor placement in modern buildings.

Recent Achievements in Research on Thermal Comfort and Ventilation in the Aspect of Providing People with Appropriate Conditions in Different Types of Buildings—Semi-Systematic Review

“…In terms of ventilation, there was both natural [31][32][33] and controlled [28][29][30]34] ventilation, with virtually no predominance of either system, which indicates the commonness of both types of ventilation in office buildings. In the analyses, measurements of air parameters in spaces [31,33] and measurements along with surveys on the feelings of their users [30,34] prevailed, but research works based on simulations [32], transfer learning [29], and a combination of simulations in EnergyPlus and machine learning algorithms [28] appeared. The discussed studies have many limitations given by their authors, among which one can specify the study of individual thermal comfort optimizing [28] or the study of only one office [29,30,32,33].…”

“…In the paper [28], Peng B. and S.J. Hsieh noted that thermal comfort measurements are often simplified and do not provide adequate control, and traditional control models are unable to adjust indoor environmental conditions to individual requirements.…”

“…In studies on offices, large, open spaces are most often studied [28,[30][31][32][33][34]; sometimes the same offices in different locations are studied [31]. In the case of the research in [28], 52 spaces located in 26 cities from "ASHRAE RP-884 Database", 57 cities located in 30 countries from "The Scales Project Dataset", and 1 building (24 participants: 16 females, 8 males) from Medium US Office were numerically analyzed, and in [30] three different offices were analyzed.…”

“…In studies on offices, large, open spaces are most often studied [28,[30][31][32][33][34]; sometimes the same offices in different locations are studied [31]. In the case of the research in [28], 52 spaces located in 26 cities from "ASHRAE RP-884 Database", 57 cities located in 30 countries from "The Scales Project Dataset", and 1 building (24 participants: 16 females, 8 males) from Medium US Office were numerically analyzed, and in [30] three different offices were analyzed. In terms of ventilation, there was both natural [31][32][33] and controlled [28][29][30]34] ventilation, with virtually no predominance of either system, which indicates the commonness of both types of ventilation in office buildings.…”

“…In the case of the research in [28], 52 spaces located in 26 cities from "ASHRAE RP-884 Database", 57 cities located in 30 countries from "The Scales Project Dataset", and 1 building (24 participants: 16 females, 8 males) from Medium US Office were numerically analyzed, and in [30] three different offices were analyzed. In terms of ventilation, there was both natural [31][32][33] and controlled [28][29][30]34] ventilation, with virtually no predominance of either system, which indicates the commonness of both types of ventilation in office buildings. In the analyses, measurements of air parameters in spaces [31,33] and measurements along with surveys on the feelings of their users [30,34] prevailed, but research works based on simulations [32], transfer learning [29], and a combination of simulations in EnergyPlus and machine learning algorithms [28] appeared.…”