.png){kind=icon}
返回
Performance optimization of HVAC systems with computational intelligence algorithms
.png){kind=icon}
PRE
AI
DOI：10.1016/j.enbuild.2014.06.021.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A model for minimization of HVAC energy consumption and room temperature ramp rate is presented. A data-driven approach is employed to construct the relationship between input and output parameters using data collected from a commercial building. Computational intelligence algorithms are applied to solve the non-parametric model. Experiments are conducted to analyze performance of the three computational intelligence algorithms. The experiment results indicate that particle swarm optimization and harmony search algorithms are suitable for solving the proposed optimization model. Three case studies of HVAC performance optimization based on simulation are presented. The computational results demonstrate that simultaneous minimization of energy and room temperature ramp rate is more beneficial than minimization of energy only. The proposed approach is implemented to demonstrate its capability of saving energy. (C) 2014 Elsevier B.V. All rights reserved.
Keyword:
Energy optimization
HVAC system
Data mining
Evolutionary algorithm
Particle swarm optimization
Harmony search 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:
7.1
论文数:
1.6W
被引数:
6.8W
机构
引用论文
.png){kind=icon}
Multiobjective evolutionary algorithms: A comparative case study and the Strength Pareto approach多目标进化算法: 比较案例研究和强度帕累托方法
PREAI
分享收藏
查看更多内容