.png){kind=icon}
返回
A PSO-based algorithm for optimal multiple chiller systems operation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.applthermaleng.2011.08.008.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In Heating, Ventilation and Air Conditioning (I-IVAC) plants of medium-high cooling capacity, multiple chiller systems are often employed. System performance is evaluated in terms of user comfort, energy use, and financial costs. The problem of efficiently managing multiple chiller systems is complex in many respects. In particular, the electrical energy consumption markedly increases if the machines are not properly managed. Therefore significant energy savings can be achieved by optimizing chiller operation. In this paper an unified method for efficient management of multiple chiller systems, by means of a Particle Swarm Optimization (PSO) based algorithm, is presented. In particular, the results show that it is possible to achieve substantial energy savings while granting good load profile tracking with respect to standard approaches. The performance of the algorithm is evaluated by resorting to a simulation environment, where the plant dynamics are accurately described. The results show that PSO satisfactorily deals with such kind of nonlinear constrained optimization problem, while offering advantages, with respect to other optimization techniques, in terms of ease of implementation. (C) 2011 Elsevier Ltd. All rights reserved.
Keyword:
Multiple chiller
Optimal chiller loading
Optimal chiller sequencing
PSO algorithm
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.9
论文数:
2.8W
被引数:
10.6W
机构
引用论文
Optimal chiller loading by differential evolution algorithm for reducing energy consumption降低能耗的差分进化算法优化冷水机组负荷.png)
ENERGY AND BUILDINGS
ENERGY AND BUILDINGSIF7.1
PREAI
分享收藏分享收藏Optimal chiller loading by genetic algorithm for reducing energy consumption降低能耗的遗传算法优化冷水机组负荷ENERGY AND BUILDINGS
ENERGY AND BUILDINGSIF7.1
PREAI
分享收藏
分享收藏Supergene gold characterization by geochemistry, grain morphology and Au-Ag-Cu-Te classification
PREAI
分享收藏A multi-phase genetic algorithm for the efficient management of multi-chiller systems用于高效管理多冷水机组系统的多阶段遗传算法
PREAI
分享收藏 没有更多内容
没有更多内容