Prediction of the performance of a solar thermal energy system using adaptive neuro-fuzzy inference system

This study investigates the applicability of adaptive neuro-fuzzy inference system (ANFIS) approach for predicting the performance parameters of a solar thermal energy system (STES). Experiments were conducted on the STES during the summer season and for different Canadian weather conditions in Ottawa. The experimental data were used for training and testing the ANFIS network model. The model was then optimised. The predicted values were found to be in very good agreement with the experimental values with mean relative error less than 0.18% and 3.26% for the preheat tank stratification temperatures and the solar fractions, respectively. The results demonstrate that the ANFIS approach can provide high accuracy and reliability for predicting the performance of thermal energy systems.

[1]  S. Kalogirou,et al.  Artificial Intelligence Techniques in Solar Energy Applications , 2010 .

[2]  Evgueniy Entchev,et al.  Experimental and Simulation Study on a Solar Domestic Hot Water System With Flat-Plate Collectors for the Canadian Climatic Conditions , 2012 .

[3]  S. Kalogirou.  Solar Energy Engineering: Processes and Systems , 2009 .

[4]  Arzu Şencan Şahin,et al.  Performance analysis of single-stage refrigeration system with internal heat exchanger using neural network and neuro-fuzzy , 2011 .

[5]  Robert Babuska,et al.  Fuzzy Modeling for Control , 1998 .

[6]  Chuen-Tsai Sun,et al.  Neuro-fuzzy modeling and control , 1995, Proc. IEEE.

[7]  Abraham Kandel,et al.  Neuro-Fuzzy Pattern Recognition , 2000 .

[8]  Mustafa Inalli,et al.  Modelling a ground-coupled heat pump system using adaptive neuro-fuzzy inference systems , 2008 .

[9]  Mohsen Hayati,et al.  Prediction of the heat transfer rate of a single layer wire-on-tube type heat exchanger using ANFIS , 2009 .

[10]  Bart Kosko,et al.  Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence , 1991 .

[11]  Y. Varol,et al.  Prediction of flow fields and temperature distributions due to natural convection in a triangular enclosure using Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) , 2007 .

[12]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[13]  Saad Mekhilef,et al.  A review on solar energy use in industries , 2011 .

[14]  Michael Conlon,et al.  Validated TRNSYS model for forced circulation solar water heating systems with flat plate and heat pipe evacuated tube collectors , 2011 .

[15]  Shigeo Abe,et al.  Neural Networks and Fuzzy Systems , 1996, Springer US.