Optimal Type-2 Fuzzy Controller For HVAC Systems

Khooban, Mohammad Hassan; Abadi, Davood Nazari Maryam; Alfi, Alireza; Siahi, Mehdi

doi:10.7305/automatika.2014.01.219

Cited by 42 publications

(16 citation statements)

References 25 publications

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“…The processing time required for multilayered NN can be a drawback for this type of controllers. An optimal intelligent controller by using fuzzy controller and particle swarm optimization (PSO) can be used for HVAC systems (Khooban et al 2012). The aim of the study was to control the air supply pressure of an HVAC system.…”

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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Section: Hvac Controlmentioning

confidence: 99%

Computational intelligence techniques for HVAC systems: A review

et al. 2016

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“…Some studies applied PSO algorithm to design Fig. 7 Trend of PSO studies efficient HVAC systems [89][90][91]. Wang et al [92] used PSO algorithm to analyze the energy flow of the conventional separation production (SP) system and the redundant BCHP system.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Recent Advances in Swarm Intelligence and Evolutionary Computation

Yang¹

2015

Studies in Computational Intelligence

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The series "Studies in Computational Intelligence" (SCI) publishes new developments and advances in the various areas of computational intelligence-quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Of particular value to both the contributors and the readership are the short publication timeframe and the world-wide distribution, which enable both wide and rapid dissemination of research output.More information about this series at

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“…The PSO algorithm is a partly new populationbased heuristic optimization method which is based on a metaphor of social interaction, specifically bird flocking (Khooban et al, 2012(Khooban et al, , 2013a. The main benefits of PSO are:…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Application of Particle Swarm Optimization for Transmission Network Expansion Planning with Security Constraints

Sarrafan¹

2014

RJASET

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In this study, a new discrete parallel Particle Swarm Optimization (PSO) method is presented for long term Transmission Network Expansion Planning (TNEP) with security constraints. The procedure includes obtaining the expansion planning with the minimum investment cost using a model based on DC load flow formulation. (N-1) contingency is included in this model. The Particle Swarm Optimization algorithm presented in this study is used to solve the planning problem for two different models: without security constraints and with security constraints. Also to solve the problem of transmission expansion planning for a medium network, new improved particle swarm optimization algorithms, the so called, Parallel Particle Swarm Optimization (PPSO) is proposed in this research. The algorithm presents high performances for such networks. The performances with this new algorithm are shown to be better than the ones with the standard PSO. Simulation results show the effectiveness of the parallel particle swarm optimization algorithm.

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Optimal Type-2 Fuzzy Controller For HVAC Systems

Cited by 42 publications

References 25 publications

Computational intelligence techniques for HVAC systems: A review

Computational intelligence techniques for HVAC systems: A review

Recent Advances in Swarm Intelligence and Evolutionary Computation

Application of Particle Swarm Optimization for Transmission Network Expansion Planning with Security Constraints

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