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
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.
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.
-
1Ole Kristensen,刘旖祺.如何保证区域供热系统的寿命[J].区域供热,2005(6):47-50.
-
2徐明才,魏万利.城市供热综合管理系统简介[J].应用能源技术,2000(3):43-44. 被引量:1
-
3周咏梅,阳爱民,沈智慧.一种基于神经网络的模糊推理和规则生成方法[J].计算机工程与应用,2004,40(13):49-51. 被引量:10
-
4陈东祥,王刚,吕世霞.Neuro-fuzzy predictive control for nonlinear application[J].Journal of Harbin Institute of Technology(New Series),2008,15(6):763-766.
-
5方甜莲,贾立.Identification of Neuro-Fuzzy Hammerstein Model Based on Probability Density Function[J].Journal of Donghua University(English Edition),2016,33(5):703-707.
-
6高海涛,蔡启仲.基于TMS320F2812的变积分型模糊PID供热控制系统[J].仪表技术与传感器,2010(1):89-91.
-
7Francisco Javier Luna Rosas,Julio Cesar Martinez Romo,Ricardo Mendoza Gonzalez,Valentin Lopez Rivas,Miguel Mora Gonzalez,Gricelda Medina Veloz.A Neuro-Fuzzy Approach for Automatic Detection of Breast Cancer Based on Raman Spectroscopy[J].通讯和计算机(中英文版),2014,11(2):158-167.
-
8Fabrizio Padula,Antonio Visioli.Set-point Filter Design for a Two-degree-of-freedom Fractional Control System[J].IEEE/CAA Journal of Automatica Sinica,2016,3(4):451-462.
-
9刘怀国,孙建华,张冰,张尤赛.ANFIS及其在控制系统中的应用[J].华东船舶工业学院学报,2001,15(5):27-31. 被引量:13
-
10王保平,李宗领,谢维信.基于区域信息的模糊加权图像恢复方法[J].计算机工程,2003,29(19):11-12.