An interactive hybrid cooling system featuring personalized controls with adaptive thermal comfort with non-intrusive sensing techniques
Defense by Siliang Lu,
Friday, 17 May 2019 | 2:00-4:00pm
MMCH 415 IW Conference Room
Siliang Lu’s Advisory Committee, listed below, sincerely hope that you will join them for this presentation, the final step in PhD Candidacy.
Dr. Erica Cochran Hameen (Chair), Assistant Professor, School of Architecture, Carnegie Mellon University
Dr. Omer Tugrul Karaguzel, Assistant Professor, School of Architecture, Carnegie Mellon University
Dr. Berangere Lartigue, Associate Professor, Laboratoire PHASE, Paul Sabatier University
Heating, ventilation and air-conditioning (HVAC) systems play a key role in shaping a building’s performance. Effective and efficient HVAC operations help maximize energy savings and can help create a comfortable indoor environment for occupants. Throughout the world, open plan office environments are a popular and cost effective design solution in commercial buildings. While open plan offices can be a cost effective solution, Architects and Engineers are challenged with identifying solutions that are both energy efficient and provide satisfactory indoor temperature and humidity levels that meet a variety of occupant comfort desires.
Responding to this challenge requires the development of a new paradigm for HVAC system frameworks. This research proposes a new non-intrusiveness sensing framework for monitoring adaptive thermal comfort in real-time in the form of an integrative personalized hybrid cooling system for open-plan office buildings. The research consists of four parts:
The development of an adaptive personalized cooling system utilizing a desk fan to improve the local thermal environment automatically with non-intrusive sensing techniques and machine learning algorithms. The sensing system consists of indoor air temperature and relative humidity sensors, air velocity sensors, and infrared sensors.
Quantification of the potential energy savings from the new paradigm where multiple personalized cooling systems are connected to optimize the cooling set-point so that the set-point dead band can be increased to save energy.
Development of a data-driven approach with a Computational Fluid Dynamics (CFD) simulator to analyze and identify the energy savings in a typical open office plan while maintaining acceptable thermal comfort values.
Development of an energy co-simulation with the proposed hybrid cooling system to analyze the energy benefits in a typical open office plan while maintaining acceptable thermal comfort values utilizing the ASHRAE RP-884 database to simulate occupant thermal comfort values.
The research results identified up to a 14% energy savings compared to the baseline in the field study. Additionally, the machine learning algorithms were successful in providing 80% of the test subjects with a desired thermal comfort range automatically with few occupant override actuation.
Overall, the proposed hybrid cooling system featuring an adaptive personalized cooling system can not only optimize energy performance, but also provides the opportunity for increased occupant thermal comfort in open-plan office buildings.
Keywords: Adaptive thermal comfort; Adaptive personalized cooling system; Non-intrusive infrared sensing; Hybrid cooling system.
The complete proposal document is available here: