A cost‐effective operating strategy to reduce energy consumption in a HVAC system
ABSTRACT The operation of the building heating, ventilating, and air conditioning (HVAC) system is a critical activity in terms of optimizing the building's energy consumption, ensuring the occupants' comfort, and preserving air quality. The performance of HVAC systems can be improved through optimized supervisory control strategies. Set points can be adjusted by the optimized supervisor to improve the operating efficiency. This paper presents a cost-effective building operating strategy to reduce energy costs associated with the operation of the HVAC system. The strategy determines the set points of local-loop controllers used in a multi-zone HVAC system. The controller set points include the supply air temperature, the supply duct static pressure, and the chilled water supply temperature. The variation of zone air temperatures around the set point is also considered. The strategy provides proper set points to controllers for minimum energy use while maintaining the required thermal comfort. The proposed technology is computationally simple and suitable for online implementation; it requires access to some data that are already measured and therefore available in most existing building energy management and control systems. The strategy is evaluated for a case study in an existing variable air volume system. The results show that the proposed strategy may be an excellent means of reducing utility costs associated with maintaining or improving indoor environmental conditions. It may reduce energy consumption by about 11% when compared with the actual strategy applied on the investigated existing system. Copyright © 2007 John Wiley & Sons, Ltd.
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ABSTRACT: A model for minimization of HVAC energy consumption and room temperature ramp rate is presented. A data-driven approach is employed to construct the relationship between input and output parameters using data collected from a commercial building. Computational intelligence algorithms are applied to solve the non-parametric model. Experiments are conducted to analyze performance of the three computational intelligence algorithms. The experiment results indicate that particle swarm optimization and harmony search algorithms are suitable for solving the proposed optimization model. Three case studies of HVAC performance optimization based on simulation are presented. The computational results demonstrate that simultaneous minimization of energy and room temperature ramp rate is more beneficial than minimization of energy only. The proposed approach is implemented to demonstrate its capability of saving energy.Energy and Buildings 10/2014; 81:371–380. DOI:10.1016/j.enbuild.2014.06.021 · 2.47 Impact Factor
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ABSTRACT: Modern manufacturing facilities waste many energy savings opportunities (ESO) due to the lack of integration between the facility and the production system. To explore the energy savings opportunities, this paper combines the two largest energy consumers in a manufacturing plant: the production line and the heating, ventilation, and air conditioning (HVAC) system. The concept of the energy opportunity window (OW) is utilized, which allows each machine to be turned off at set periods of time without any throughput loss. The recovery time of each machine is the minimum amount of time a machine must be operational between opportunity windows to guarantee zero production loss and it is explored both analytically and numerically. The opportunity window for the production line is synced with the peak periods of energy demand for the HVAC system to create a heuristic rule to optimize the energy cost savings. This integrated system is modeled and tested using simulation studies.IEEE Transactions on Automation Science and Engineering 01/2014; 11(3):1545-5955. DOI:10.1109/TASE.2013.2284915 · 2.16 Impact Factor
Energy and Buildings 12/2014; 85:536 - 548. DOI:10.1016/j.enbuild.2014.09.055 · 2.47 Impact Factor