期刊文献+

A Neuro-Fuzzy Based Adaptive Set-Point Heat Exchanger Control Scheme in District Heating System

A Neuro-Fuzzy Based Adaptive Set-Point Heat Exchanger Control Scheme in District Heating System
下载PDF
导出
摘要 The control of heat exchange stations in district heating system is critical for the overall energy efficiency and can be very difficult due to high level of complexity. A conventional method is to control the equipment such that the temperature of hot water supply is maintained at a set-point that may be a fixed value or be compensated against the external temperature. This paper presents a novel scheme that can determine the optimal set-point of hot water supply that maximizes the energy efficiency whilst providing sufficient heating capacity to the load. This scheme is based on Adaptive Neuro-Fuzzy Inferential System. The aim of this study is to improve the overall performance of district heating systems.
出处 《Journal of Civil Engineering and Architecture》 2012年第11期1584-1588,共5页 土木工程与建筑(英文版)
关键词 District heating system NEURO-FUZZY inferential sensor energy efficiency CONTROL 自适应神经模糊推理系统 控制方案 集中供热系统 模糊自适应 换热器 区域供热系统 点式 能源利用效率
  • 相关文献

参考文献13

  • 1Z. Liao and A. L. Dexter, A simplified physical model for estimating the average air temperature in multi-zone heating systems, Building and Environment 39 (9) (2004) 1013-1022.
  • 2L. Zadeh, Outline of a new approach to the analysis of complex systems and decision processes, in: IEEE Transactions on System, Man, and Cybernetics, Browse Journals & Magazines 3 (1) (1973) 28-44.
  • 3A. L. Dexter and D. W. Trewhella, Building control systems: fuzzy rule-based approach to performance assessment, Building Services Research and Technology 11 (4) (1990) 115-124.
  • 4A. I. Dounis, M. J. Santamouris and C. C. Lcfas, Building visual comfort control with fuzzy reasoning, Energy Conservation and Management 34 (1) (1993) 17-28.
  • 5A. I. Dounis, M. Bruant, M. Santamouris, O. Guaraccino and P. Michel, Comparison of conventional and fuzzy control of indoor air quality in buildings, Journal of Intelligent and Fuzzy Systems 4 (1996) 131 - 140.
  • 6P. Angelov, A fuzzy approach to building thermal systems optimization, Vol. 2, in: Proceedings of the eighth IFSA World congress, Taipai, Taiwan, 1999, pp. 528-531.
  • 7J. F. Kreider, Neural networks applied to building energy studies, in: H. Bloem (Ed.), Workshop on Parameter Identification, Joint Research Center, Ispra, 1995, pp. 233-251.
  • 8S. J. Hepeworth and A. L. Arthur, Adaptive neural control with stable learning, Mathematics and Computers in Simulation 41 (2000) 39-51.
  • 9M. S. Moheseni, B. Thomas and P. Fahlen, Estimation of operative temperature in buildings using artificial neural networks, Energy and Buildings 38 (2006) 635-640.
  • 10S. Jassar, Z. Liao and L. Zhao, Adaptive neuro-fuzzy based inferential sensor model for estimating the average air temperature in space heating systems, Building and Environment 44 (8) (2009) 1609-1616.

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部