.png){kind=icon}
返回
HVAC system optimization - in-building section
.png){kind=icon}
PRE
AI
DOI：10.1016/j.enbuild.2003.12.007.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents a practical method to optimize in-building section of centralized Heating, Ventilation and Air-conditioning (HVAC) systems which consist of indoor air loops and chilled water loops. First, through component characteristic analysis, mathematical models associated with cooling loads and energy consumption for heat exchangers and energy consuming devices are established. By considering variation of cooling load of each end user, adaptive neuro-fuzzy inference system (ANFIS) is employed to model duct and pipe networks and obtain optimal differential pressure (DP) set points based on limited sensor information. A mix-integer nonlinear constraint optimization of system energy is formulated and solved by a modified genetic algorithm. The main feature of our paper is a systematic approach in optimizing the overall system energy consumption rather than that of individual component. A simulation study for a typical centralized HVAC system is provided to compare the proposed optimization method with traditional ones. The results show that the proposed method indeed improves the system performance significantly. (C) 2004 Elsevier B.V. All rights reserved.
Keyword:
HVAC system
optimization
energy conservation
simulation
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:
7.1
论文数:
1.6W
被引数:
6.8W
机构
暂无机构信息
引用论文
Global optimization of absorption chiller system by genetic algorithm and neural network.png)
ENERGY AND BUILDINGS
ENERGY AND BUILDINGSIF7.1
PREAI
分享收藏Optimal control development for chilled water plants using a quadratic representationENERGY AND BUILDINGS
ENERGY AND BUILDINGSIF7.1
PREAI
分享收藏
Performance validation and energy analysis of HVAC systems using simulationENERGY AND BUILDINGS
ENERGY AND BUILDINGSIF7.1
PREAI
分享收藏Fuzzy programming for multiobjective 0-1 programming problems through revised genetic algorithms
PREAI
分享收藏 没有更多内容
没有更多内容