暖通空调
热舒适性
可穿戴计算机
空调
能源消耗
高效能源利用
计算机科学
楼宇自动化
汽车工程
能量(信号处理)
控制系统
模拟
控制工程
工程类
嵌入式系统
电气工程
机械工程
统计
物理
数学
热力学
作者
Seonghun Cho,Hong Jae Nam,Chuanqi Shi,Choong Yeon Kim,Sanghyuk Byun,Karen‐Christian Agno,Byung Chul Lee,Jianliang Xiao,Joo Yong Sim,Jae‐Woong Jeong
标识
DOI:10.1016/j.bios.2022.115018
摘要
The conventional heating, ventilation, and air conditioning (HVAC) systems are based on a set-point control approach that only considers the temperature of the environment without reflecting the thermophysiological status of the occupant. This approach not only fails to fully satisfy individual thermal preferences, but it also makes an HVAC operation energy-inefficient. One possible solution is to control the indoor thermal condition based on an accurate prediction of the occupant's thermal comfort to prevent any unnecessary energy consumption. Here, we present an artificial intelligence (AI) wearable sensor-based human-in-the-loop HVAC control system that is operated on a real-time basis reflecting the thermophysiological condition of the occupant to automatically improve their thermal comfort while reducing the energy consumption of the building. The wristband-type, AI-based, three-point wearable temperature sensor offers excellent thermal comfort prediction accuracy (93.9%), enabling a human-centric HVAC control operation. A proof-of-concept demonstration of closed human-in-the-loop HVAC control using the AI-enabled wearable sensor system confirms both the accuracy of the thermal comfort prediction and the energy-efficiency of this approach, demonstrating its potential as a new solution that improves the occupant's thermal comfort and provides building energy savings.
科研通智能强力驱动
Strongly Powered by AbleSci AI