ANN and ANFIS models for performance evaluation of a vertical ground source heat pump system

Authors:
Hikmet Esen;Mustafa Inalli
Affiliations:
Department of Mechanical Education, Faculty of Technical Education, Fırat University, 23119 Elazığ, Turkey;Department of Mechanical Engineering, Faculty of Engineering, Fırat University, 23279 Elazığ, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 5
Cited 0

Original Contribution: A scaled conjugate gradient algorithm for fast supervised learning

Neural Networks
Fuzzy Modeling for Control

Fuzzy Modeling for Control
Wavelet transform and adaptive neuro-fuzzy inference system for color texture classification

Expert Systems with Applications: An International Journal
An expert system based on linear discriminant analysis and adaptive neuro-fuzzy inference system to diagnosis heart valve diseases

Expert Systems with Applications: An International Journal
Analysis of adaptive-network-based fuzzy inference system (ANFIS) to estimate buoyancy-induced flow field in partially heated triangular enclosures

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	12.06

Visualization

Abstract

The aim of this study is to demonstrate the comparison of an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS) for the prediction performance of a vertical ground source heat pump (VGSHP) system. The VGSHP system using R-22 as refrigerant has a three single U-tube ground heat exchanger (GHE) made of polyethylene pipe with a 40mm outside diameter. The GHEs were placed in a vertical boreholes (VBs) with 30 (VB1), 60 (VB2) and 90 (VB3)m depths and 150mm diameters. The monthly mean values of COP for VB1, VB2 and VB3 are obtained to be 3.37/1.93, 3.85/2.37, and 4.33/3.03, respectively, in cooling/heating seasons. Experimental performances were performed to verify the results from the ANN and ANFIS approaches. ANN model, Multi-layered Perceptron/Back-propagation with three different learning algorithms (the Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG) and Pola-Ribiere Conjugate Gradient (CGP) algorithms and the ANFIS model were developed using the same input variables. Finally, the statistical values are given in as tables. This paper shows the appropriateness of ANFIS for the quantitative modeling of GSHP systems.