A cost‐effective operating strategy to reduce energy consumption in a HVAC system
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.
Available from: A. Kusiak
- "The main goal of such research is to discover effective control insights with much lower computational cost. Nassif and Moujaes  investigated three combinations of controller setting points for saving energy of HVAC system based on physics models. Recently, data-driven models rather than physics-based models have been applied to investigate HVAC systems. "
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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 · 2.88 Impact Factor
Available from: Qing Chang
- "The operation and control of HVAC systems has significant impacts on the energy and cost efficiency of buildings. In , a cost effective strategy is introduced to improve energy consumption by determining set points of local loop controllers used in a multi-zone HVAC system. An optimization process is used in  to automatically change the settings of the HVAC system in accordance with changes in outside temperature and building operation. "
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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 · 2.43 Impact Factor
Available from: Enrique A. de la Cal Marín
- "However, the reduction of energy consumption in the construction of buildings has not as yet been standardized   . For example, there are different approaches to heating systems in the literature that analyse the temperature control loop in order to improve heating system energy efficiency   . Soft Computing represents a broad range of techniques that can be used to solve inexact and complex problems . "
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ABSTRACT: A novel procedure for learning Fuzzy Controllers (FC) is proposed that concerns with energy efficiency issues in distributing electrical energy to heaters in an electrical energy heating system. Energy rationalization together with temperature control can significantly improve energy efficiency, by efficiently controlling electrical heating systems and electrical energy consumption. The novel procedure, which improves the training process, is designed to train the FC, as well as to run the control algorithm and to carry out energy distribution. Firstly, the dynamic thermal performance of different variables is mathematically modelled for each specific building type and climate zone. Secondly, an exploratory projection pursuit method is used to extract the relevant features. Finally, a supervised dynamic neural network model and identification techniques are applied to FC learning and training. The FC rule-set and parameter-set learning process is a multi-objective problem that minimizes both the indoor temperature error and the energy deficit in the house. The reliability of the proposed procedure is validated for a city in a winter zone in Spain. © 2011 The Author. Published by Oxford University Press. All rights reserved.
08/2012; 20(4-4):757-769. DOI:10.1093/jigpal/jzr022
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