Artificial neural networks and adaptive neuro-fuzzy assessments for ground-coupled heat pump system

被引：231

作者：

Esen, Hikmet ^{[1
]}

Inalli, Mustafa ^{[2
]}

Sengur, Abdulkadir ^{[3
]}

Esen, Mehmet ^{[1
]}

机构：

[1] Firat Univ, Fac Tech Educ, Dept Mech Educ, TR-23119 Elazig, Turkey

[2] Firat Univ, Fac Engn, Dept Engn Mech, TR-23279 Elazig, Turkey

[3] Firat Univ, Fac Tech Educ, Dept Elect & Comp Sci, TR-23119 Elazig, Turkey

来源：

ENERGY AND BUILDINGS | 2008年 / 40卷 / 06期

关键词：

neural network; adaptive neuro-fuzzy inference system; forecast; membership functions; ground-coupled heat pump; coefficient of performance;

D O I：

10.1016/j.enbuild.2007.10.002

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

This article present a comparison of artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) applied for modelling a ground-coupled heat pump system (GCHP). The aim of this study is predicting system performance related to ground and air (condenser inlet and outlet) temperatures by using desired models. Performance forecasting is the precondition for the optimal design and energy-saving operation of air-conditioning systems. So obtained models will help the system designer to realize this precondition. The most suitable algorithm and neuron number in the hidden layer are found as Levenberg-Marquardt (LM) with seven neurons for ANN model whereas the most suitable membership function and number of membership functions are found as Gauss and two, respectively, for ANFIS model. The root-mean squared (RMS) value and the coefficient of variation in percent (cov) value are 0.0047 and 0.1363, respectively. The absolute fraction of variance (R-2) is 0.9999 which can be considered as very promising. This paper shows the appropriateness of ANFIS for the quantitative modeling of GCHP systems. (C) 2007 Elsevier B.V. All rights reserved.

引用

页码：1074 / 1083

页数：10

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