Study on application of a neuro-fuzzy models in air conditioning systems

被引：11
作者：
Costa, Herbert R. do N. [1 ]
La Neve, Alessandro [2 ]
机构：
[1] Ctr Univ FEI, Dept Comp Sci, Sao Paulo, Brazil
[2] Ctr Univ FEI, Dept Elect Engn, Sao Paulo, Brazil
来源：
SOFT COMPUTING | 2015年 / 19卷 / 04期
关键词：
Membership Function; Fuzzy Control; Fuzzy Controller; Fuzzy Inference System; Consumption Reduction;
D O I：
10.1007/s00500-014-1431-5
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, we present the application of neuro-fuzzy system-ANFIS for air conditioning systems to reduce electricity consumption. The current buildings have automation systems that provide data on lighting, electrical system, air conditioning system, etc. We studied the air conditioning system, in particular, to reduce energy consumption as air conditioning has a high consumption value. Our main goal in this study is the application of neuro-fuzzy system-ANFIS with the adjustment of the rules made by a decision tree-CART algorithm in air conditioning system. We compared the results of the application of the ANFIS-CART system with the application of PID controllers and fuzzy control system for a central air conditioning.
引用
收藏
页码：929 / 937
页数：9
相关论文
共 25 条
  • [1] Advanced control systems for single compressor chiller units
    Albieri, Michele
    Beghi, Alessandro
    Bodo, Cristian
    Cecchinato, Luca
    [J]. INTERNATIONAL JOURNAL OF REFRIGERATION-REVUE INTERNATIONALE DU FROID, 2009, 32 (05): : 1068 - 1076
  • [2] [Anonymous], 2003, Handbook of HVAC Applications
  • [3] The "extended building energy hub": A new method for the simultaneous optimization of energy demand and energy supply in buildings
    Bayraktar, Meltem
    Fabrizio, Enrico
    Perino, Marco
    [J]. HVAC&R RESEARCH, 2012, 18 (1-2): : 67 - 87
  • [4] Optimal chiller loading by genetic algorithm for reducing energy consumption
    Chang, YC
    Lin, JK
    Chuang, MH
    [J]. ENERGY AND BUILDINGS, 2005, 37 (02) : 147 - 155
  • [5] Sequencing of chillers by estimating chiller power consumption using artificial neural networks
    Chang, Yung-Chung
    [J]. BUILDING AND ENVIRONMENT, 2007, 42 (01) : 180 - 188
  • [6] Cordon O., 2001, GENETIC FUZZY SYSTEM
  • [7] Costa HRN, 1997, THESIS DEP ELECT ENG
  • [8] Cox E., 1994, FUZZY SYSTEMS HDB PR
  • [9] Haykin S., 2009, Neural networks and learning machines, V3rd
  • [10] Hossein Mirinejad, 2012, 2012 IEEE ENERGYTECH, P1, DOI DOI 10.1109/ENERGYTECH.2012.6304679
123