Thermal comfort control on multi-room fan coil unit system using LEE-based fuzzy logic

Chu, Chi Min; Jong, Tai-Lang; Huang, Yuwei

doi:10.1016/j.enconman.2004.08.002

Cited by 50 publications

(20 citation statements)

References 4 publications

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“…Both controllers were able to reject external disturbances caused by the outdoor air temperature. Chu et al (2005) proposed a least enthalpy estimator (LEE) based fuzzy control system for a fan coil unit. The controller integrated the concept of thermal comfort with the theory of enthalpy.…”

Section: Controlmentioning

confidence: 99%

“…CI is a rapidly advancing research field and includes a collection of various computation techniques, including but not limited to: expert systems, genetic algorithm (GA), artificial neural network (ANN), support vector machines (SVM). The most commonly used CI techniques for HVAC applications are fuzzy logic (Chu et al 2005;So et al 1997;Zheng and Xu 2004), ANN (Argiriou et al 2000;Curtiss et al 1994;Kanarachos and Geramanis 1998), GA (Lu et al 2005;Mossolly et al 2009;Nassif et al 2005;Wang and Jin 2000;Wright et al 2002), multi-agent systems (Hagras et al 2008;Rutishauser et al 2005;Yang and Wang 2013) and pattern recognitionbased methods (Du et al 2007a;Hu et al 2012;Naja. et al 2012;Wang and Cui 2005;Wang and Xiao 2004a;Zhao et al 2013b).…”

Section: Algorithmsmentioning

confidence: 99%

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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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Section: Controlmentioning

confidence: 99%

Section: Algorithmsmentioning

confidence: 99%

Computational intelligence techniques for HVAC systems: A review

et al. 2016

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“…• Piao et al (1998) used a simple fuzzy adaptive controller to achieve temperature control in HVAC. • Chu et al (2005) proposed a least enthalpy estimator (LEE) that combines the definition of thermal comfort level (TCL) for load prediction in order to provide timely suitable settings for a fan coil unit (FCU) fuzzy controller used in HVAC. The controller is divided into two fuzzy rule sets where the inputs of the first fuzzy rule set express room thermal load.…”

Section: State Of the Artmentioning

confidence: 99%

Artificial Intelligence in Buildings: A Review of the Application of Fuzzy Logic

Κολοκότσα¹

2007

Advances in Building Energy Research

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“…Simulation and experimental studies have been reported that thermal comfort control can help achieve better thermal comfort, and a higher energy efficiency and system reliability [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Indoor thermal comfort characteristics under the control of a direct expansion air conditioning unit having a variable-speed compressor and a supply air fan

Deng

2009

Applied Thermal Engineering

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Thermal comfort control on multi-room fan coil unit system using LEE-based fuzzy logic

Cited by 50 publications

References 4 publications

Computational intelligence techniques for HVAC systems: A review

Computational intelligence techniques for HVAC systems: A review

Artificial Intelligence in Buildings: A Review of the Application of Fuzzy Logic

Indoor thermal comfort characteristics under the control of a direct expansion air conditioning unit having a variable-speed compressor and a supply air fan

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