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A platform for research: civil engineering, architecture and urbanism
Optimal chiller loading by differential evolution algorithm for reducing energy consumption
Abstract This study employs differential evolution algorithm to solve the optimal chiller loading problem for reducing energy consumption. To testify the performance of the proposed method, the paper adopts two case studies to compare the results of the developed optimal model with those of the Lagrangian method, genetic algorithm and particle swarm algorithm. The result shows that the proposed differential evolution algorithm can find the optimal solution as the particle swarm algorithm can, but obtain better average solutions. Moreover, it outperforms the genetic algorithm in finding optimal solution and also overcomes the divergence problem caused by the Lagrangian method occurring at low demands.
Optimal chiller loading by differential evolution algorithm for reducing energy consumption
Abstract This study employs differential evolution algorithm to solve the optimal chiller loading problem for reducing energy consumption. To testify the performance of the proposed method, the paper adopts two case studies to compare the results of the developed optimal model with those of the Lagrangian method, genetic algorithm and particle swarm algorithm. The result shows that the proposed differential evolution algorithm can find the optimal solution as the particle swarm algorithm can, but obtain better average solutions. Moreover, it outperforms the genetic algorithm in finding optimal solution and also overcomes the divergence problem caused by the Lagrangian method occurring at low demands.
Optimal chiller loading by differential evolution algorithm for reducing energy consumption
Lee, Wen-Shing (author) / Chen, Yi-Ting (author) / Kao, Yucheng (author)
Energy and Buildings ; 43 ; 599-604
2010-10-18
6 pages
Article (Journal)
Electronic Resource
English
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