Neuro-optimal operation of a variable air volume HVAC&amp;R system

Ning, Min; Zaheeruddin, Mohammed

doi:10.1016/j.applthermaleng.2009.10.009

Cited by 41 publications

(10 citation statements)

References 7 publications

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“…An NN-based optimization method was developed by Ning and Zaheeruddin (2010). The method integrated an NN-based optimization technique with a model-based prediction.…”

Section: Hvac Controlmentioning

confidence: 99%

Computational intelligence techniques for HVAC systems: A review

et al. 2016

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Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO 2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air-conditioning (HVAC) systems are the major source of energy consumption in buildings and ideal candidates for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems. The analysis of trends reveals that the minimisation of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences.Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE-2, HVACSim+ and ESP-r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on multi-agent systems (MAS), as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions.

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“…An NN-based optimization method was developed by Ning and Zaheeruddin (2010). The method integrated an NN-based optimization technique with a model-based prediction.…”

Section: Hvac Controlmentioning

confidence: 99%

Computational intelligence techniques for HVAC systems: A review

et al. 2016

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show abstract

“…With the power consumption of a pump given in Eq. (6), ignoring the mechanical losses, the total one of all pumps is…”

Section: Thermal Resistance-based Methods For Hen Optimizationmentioning

confidence: 99%

“…For instance, Wang and Burnett proposed a pressure set point control method for an indirect water-cooled chilling system [2], and Lu et al raised an optimization method for a heat, ventilation and air condition (HVAC) system with the duct differential pressure set point [3]. Besides, the pressure differentials of other set points also work in the optimal operating of HENs, such as the pressure differentials of secondary variable speed pumps [4,5], the static pressures of fans [6], and the pressure drops through heat exchangers [2]. However, the direct control parameters in a HEN are usually the openings of valves, rather than the pressure differentials of some set points.…”

Section: Introductionmentioning

confidence: 98%

Direct optimal control of valve openings in heat exchanger networks and experimental validations

Wang

Chen

2015

International Journal of Heat and Mass Transfer

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“…For instance, a supervisory optimal control strategy based on an artificial neural network was developed to find several optimal set points including the supply air static pressure. Significant energy (10% under full load condition, 19% under partial load condition) was saved according to the simulation results [20]. Another new control strategy was developed for VAV terminals to save energy.…”

Section: Introductionmentioning

confidence: 99%