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 40 mm outside diameter. The GHEs were placed in a vertical boreholes (VBs) with 30 (VB1), 60 (VB2) and 90 (VB3) m depths and 150 mm 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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
