A multi-occupants' comfort-driven and energy-efficient control strategy of VAV system based on learned thermal comfort profiles. (26th November 2018)

• Record Type: Journal Article
• Authors: Xu, Yuli; Chen, Sheng; Javed, Muhammad; Li, Ning; Gan, Zhongxue
• Is Part Of: Science and technology for the built environment. Volume 24:Number 10(2018)
• Page Start: 1141
• Page End: 1149
• Publication Date: 2018-11-26
• Subjects: Heating -- Periodicals; Ventilation -- Periodicals; Air conditioning -- Periodicals; Refrigeration and refrigerating machinery -- Periodicals; Indoor air quality -- Periodicals; Indoor air quality; Air conditioning; Heating; Refrigeration and refrigerating machinery; Ventilation; Periodicals; 697
• DOI: 10.1080/23744731.2018.1474690
• Languages: English
• ISSNs: 2374-474X

Abstract

Abstract : For heating, ventilation, and air-conditioning (HVAC) systems, thermal comfort and energy saving always contradict each other. This article proposes a personalized feedback-data-based learning approach to quantify thermal comfort in a complaint-driven environment control system. We apply a machine learning algorithm named softmax regression to convert user votes into a probability distribution and fit the distribution to data sets for three different comfort conditions (i.e., uncomfortably warm, comfortable, and uncomfortably cold). We have also taken into account the multi-occupants condition under which different users have different thermal preferences and attained an optimized zone-level temperature set-point that is agreeable to all occupants. Co-simulation EnergyPlus/simulink demonstrates the satisfying performance and feasibility of energy saving potentials of the model in real environment control.