Optimal Chiller Loading by Team Particle Swarm Algorithm for Reducing Energy Consumption

Energy saving is an important issue for multiple-chiller systems. Optimal chiller loading (OCL) in multiple-chiller systems has been investigated with many optimization algorithms to save energy. Particle swarm optimization (PSO) algorithm has been successful in solving this problem in some cases, b...

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Autores principales:	Wen-Shing Lee, Wen-Hsin Lin, Chin-Chi Cheng, Chien-Yu Lin
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	optimal chiller loading (OCL) problem particle swarm optimization (PSO) energy consumption team particle swarm optimization (TPSO) Technology T
Acceso en línea:	https://doaj.org/article/6857693fbfd045bc915e53d2ad5ecfe7
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spelling	oai:doaj.org-article:6857693fbfd045bc915e53d2ad5ecfe72021-11-11T15:52:26ZOptimal Chiller Loading by Team Particle Swarm Algorithm for Reducing Energy Consumption10.3390/en142170661996-1073https://doaj.org/article/6857693fbfd045bc915e53d2ad5ecfe72021-10-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/7066https://doaj.org/toc/1996-1073Energy saving is an important issue for multiple-chiller systems. Optimal chiller loading (OCL) in multiple-chiller systems has been investigated with many optimization algorithms to save energy. Particle swarm optimization (PSO) algorithm has been successful in solving this problem in some cases, but not in all. This study innovatively added a team evolution to the original particle swarm optimization algorithm, called team particle swarm optimization (TPSO). The TPSO enhances the effectiveness of original particle swarm optimization to better solve the OCL problem. The TPSO algorithm is composed of two evolutions: particle evolution and team evolution. The partial load ratio (PLR) of each operating chiller and the on-off state of each chiller are the particle evolution parameters and team evolution parameters, respectively. To evaluate the performance of the proposed method, this paper adopts three case studies so the results generated from the proposed algorithm TPSO, the original particle swarm optimization (PSO) and other recently published algorithms can be compared. In these three case studies, the optimal results generated by using TPSO algorithm are the same as those by other compared algorithms. In case 1 under 5717 RT and 5334 RT cooling load, the results generated using the TPSO are lower than those by the original PSO in the amounts of 63.35 and 79.33 kW, respectively. The results indicated that the TPSO algorithm not only enabled the optimal solution in minimizing energy consumption, but also demonstrated the best stability when compared to other algorithms. In conclusion, the presented TPSO algorithm is an efficient and promising new algorithm for solving the OCL problem.Wen-Shing LeeWen-Hsin LinChin-Chi ChengChien-Yu LinMDPI AGarticleoptimal chiller loading (OCL) problemparticle swarm optimization (PSO)energy consumptionteam particle swarm optimization (TPSO)TechnologyTENEnergies, Vol 14, Iss 7066, p 7066 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	optimal chiller loading (OCL) problem particle swarm optimization (PSO) energy consumption team particle swarm optimization (TPSO) Technology T
spellingShingle	optimal chiller loading (OCL) problem particle swarm optimization (PSO) energy consumption team particle swarm optimization (TPSO) Technology T Wen-Shing Lee Wen-Hsin Lin Chin-Chi Cheng Chien-Yu Lin Optimal Chiller Loading by Team Particle Swarm Algorithm for Reducing Energy Consumption
description	Energy saving is an important issue for multiple-chiller systems. Optimal chiller loading (OCL) in multiple-chiller systems has been investigated with many optimization algorithms to save energy. Particle swarm optimization (PSO) algorithm has been successful in solving this problem in some cases, but not in all. This study innovatively added a team evolution to the original particle swarm optimization algorithm, called team particle swarm optimization (TPSO). The TPSO enhances the effectiveness of original particle swarm optimization to better solve the OCL problem. The TPSO algorithm is composed of two evolutions: particle evolution and team evolution. The partial load ratio (PLR) of each operating chiller and the on-off state of each chiller are the particle evolution parameters and team evolution parameters, respectively. To evaluate the performance of the proposed method, this paper adopts three case studies so the results generated from the proposed algorithm TPSO, the original particle swarm optimization (PSO) and other recently published algorithms can be compared. In these three case studies, the optimal results generated by using TPSO algorithm are the same as those by other compared algorithms. In case 1 under 5717 RT and 5334 RT cooling load, the results generated using the TPSO are lower than those by the original PSO in the amounts of 63.35 and 79.33 kW, respectively. The results indicated that the TPSO algorithm not only enabled the optimal solution in minimizing energy consumption, but also demonstrated the best stability when compared to other algorithms. In conclusion, the presented TPSO algorithm is an efficient and promising new algorithm for solving the OCL problem.
format	article
author	Wen-Shing Lee Wen-Hsin Lin Chin-Chi Cheng Chien-Yu Lin
author_facet	Wen-Shing Lee Wen-Hsin Lin Chin-Chi Cheng Chien-Yu Lin
author_sort	Wen-Shing Lee
title	Optimal Chiller Loading by Team Particle Swarm Algorithm for Reducing Energy Consumption
title_short	Optimal Chiller Loading by Team Particle Swarm Algorithm for Reducing Energy Consumption
title_full	Optimal Chiller Loading by Team Particle Swarm Algorithm for Reducing Energy Consumption
title_fullStr	Optimal Chiller Loading by Team Particle Swarm Algorithm for Reducing Energy Consumption
title_full_unstemmed	Optimal Chiller Loading by Team Particle Swarm Algorithm for Reducing Energy Consumption
title_sort	optimal chiller loading by team particle swarm algorithm for reducing energy consumption
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/6857693fbfd045bc915e53d2ad5ecfe7
work_keys_str_mv	AT wenshinglee optimalchillerloadingbyteamparticleswarmalgorithmforreducingenergyconsumption AT wenhsinlin optimalchillerloadingbyteamparticleswarmalgorithmforreducingenergyconsumption AT chinchicheng optimalchillerloadingbyteamparticleswarmalgorithmforreducingenergyconsumption AT chienyulin optimalchillerloadingbyteamparticleswarmalgorithmforreducingenergyconsumption
_version_	1718433112642289664

Optimal Chiller Loading by Team Particle Swarm Algorithm for Reducing Energy Consumption

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