TR2019-143
Steady-State Analysis of HVAC Performance using Indoor Fans in Control Design
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- "Steady-State Analysis of HVAC Performance using Indoor Fans in Control Design", IEEE Conference on Decision and Control (CDC), DOI: 10.1109/CDC40024.2019.9029730, December 2019, pp. 2952-2957.BibTeX TR2019-143 PDF
- @inproceedings{Garcia2019dec,
- author = {Garcia, Joaquin and Danielson, Claus and Limon, Daniel and Bortoff, Scott A. and Di Cairano, Stefano},
- title = {Steady-State Analysis of HVAC Performance using Indoor Fans in Control Design},
- booktitle = {IEEE Conference on Decision and Control (CDC)},
- year = 2019,
- pages = {2952--2957},
- month = dec,
- doi = {10.1109/CDC40024.2019.9029730},
- url = {https://www.merl.com/publications/TR2019-143}
- }
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- "Steady-State Analysis of HVAC Performance using Indoor Fans in Control Design", IEEE Conference on Decision and Control (CDC), DOI: 10.1109/CDC40024.2019.9029730, December 2019, pp. 2952-2957.
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MERL Contacts:
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Research Areas:
Abstract:
Indoor fans are high-authority actuators in heating, ventilation, and air conditioning (HVAC) systems since they facilitate the transfer of heat between refrigerant and room air. In some variable refrigerant flow (VRF) systems, the indoor fan speeds are under the control of the occupants, rather than the HVAC control system. This paper studies the benefits of transferring control of the indoor fans to the HVAC controller. We quantify the system performance using five metrics related to occupant comfort and power consumption. The first metric measures the ability of the HVAC system to accommodate users with different temperature preferences by quantifying the largest difference in requested room temperatures that can be achieved with and without the aid of indoor fans. The second and third metrics measure the ability of the HVAC system to reject extreme heating and cooling loads. The final two metrics measure the reduction in power consumption obtained by manipulating the indoor fan speeds. Each of these metrics is computed via linear programming for varying numbers of indoor units. Simulation results indicate that the maximum steady-state difference in room temperatures is tripled, and the maximum rejected heating and cooling loads are doubled. Furthermore, power consumption is significantly reduced.
Related News & Events
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NEWS MERL researchers presented 8 papers at Conference on Decision and Control (CDC) Date: December 11, 2019 - December 13, 2019
Where: Nice, France
MERL Contacts: Scott A. Bortoff; Ankush Chakrabarty; Stefano Di Cairano
Research Areas: Control, Machine Learning, OptimizationBrief- At the Conference on Decision and Control, MERL presented 8 papers on subjects including estimation for thermal-fluid models and transportation networks, analysis of HVAC systems, extremum seeking for multi-agent systems, reinforcement learning for vehicle platoons, and learning with applications to autonomous vehicles.