Estimation and optimization of thermal performance of evacuated tube solar collector system

Abstract In this study, artificial neural networks (ANNs) and adaptive neuro-fuzzy (ANFIS) in order to predict the thermal performance of evacuated tube solar collector system have been used. The experimental data for the training and testing of the networks were used. The results of ANN are compared with ANFIS in which the same data sets are used. The R2-value for the thermal performance values of collector is 0.811914 which can be considered as satisfactory. The results obtained when unknown data were presented to the networks are satisfactory and indicate that the proposed method can successfully be used for the prediction of the thermal performance of evacuated tube solar collectors. In addition, new formulations obtained from ANN are presented for the calculation of the thermal performance. The advantages of this approaches compared to the conventional methods are speed, simplicity, and the capacity of the network to learn from examples. In addition, genetic algorithm (GA) was used to maximize the thermal performance of the system. The optimum working conditions of the system were determined by the GA.

[1]  Guofeng Yuan,et al.  A new dynamic test method for thermal performance of all-glass evacuated solar air collectors , 2012 .

[2]  Lefteri H. Tsoukalas,et al.  Fuzzy and neural approaches in engineering , 1997 .

[3]  Wenfeng Gao,et al.  Optimal tilt-angles of all-glass evacuated tube solar collectors , 2009 .

[4]  Yong Kim,et al.  Thermal performances comparisons of the glass evacuated tube solar collectors with shapes of absorber tube , 2007 .

[5]  Ercan Köse,et al.  Sliding Mode Control Based on Genetic Algorithm for WSCC Systems Include of SVC , 2013 .

[6]  Neeraj Sharma,et al.  Performance model of a novel evacuated-tube solar collector based on minichannels , 2011 .

[7]  Chitralekha Mahanta,et al.  A novel approach for ANFIS modelling based on full factorial design , 2008, Appl. Soft Comput..

[8]  Li-Chih Ying,et al.  Using adaptive network based fuzzy inference system to forecast regional electricity loads , 2008 .

[9]  LiMin Fu,et al.  Neural networks in computer intelligence , 1994 .

[10]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[11]  L. P. J. Veelenturf,et al.  Analysis and applications of artificial neural networks , 1995 .

[12]  Michael Conlon,et al.  Comparative Field Performance Study of Flat Plate and Heat Pipe Evacuated Tube Collectors (ETCs) for Domestic Water Heating Systems in a Temperate Climate , 2011 .

[13]  Mervyn Smyth,et al.  Optical evaluation and analysis of an internal low-concentrated evacuated tube heat pipe solar collector for powering solar air-conditioning systems , 2012 .

[14]  Enrico Zambolin,et al.  Experimental analysis of thermal performance of flat plate and evacuated tube solar collectors in stationary standard and daily conditions , 2010 .

[15]  Ahmad Houri,et al.  Quantification of energy produced from an evacuated tube water heater in a real setting , 2013 .

[16]  Baccoli Roberto,et al.  Graybox and adaptative dynamic neural network identification models to infer the steady state efficiency of solar thermal collectors starting from the transient condition , 2010 .

[17]  Enrico Zambolin,et al.  An improved procedure for the experimental characterization of optical efficiency in evacuated tube solar collectors , 2012 .

[18]  Soteris A. Kalogirou,et al.  Prediction of flat-plate collector performance parameters using artificial neural networks , 2006 .

[19]  Wenfeng Gao,et al.  Comparative studies on thermal performance of water-in-glass evacuated tube solar water heaters with different collector tilt-angles , 2011 .

[20]  Soteris A. Kalogirou,et al.  Solar thermal collectors and applications , 2004 .

[21]  Siddhartha,et al.  Thermal performance optimization of a flat plate solar air heater using genetic algorithm , 2010 .

[22]  Mario Vanhoucke,et al.  A genetic algorithm for the preemptive and non-preemptive multi-mode resource-constrained project scheduling problem , 2010, Eur. J. Oper. Res..

[23]  Soteris A. Kalogirou,et al.  Modelling of an ICS solar water heater using artificial neural networks and TRNSYS , 2009 .

[24]  Xing Zhang,et al.  An experimental study on evacuated tube solar collector using supercritical CO2 , 2008 .

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

[26]  Ruobing Liang,et al.  Thermal performance analysis of the glass evacuated tube solar collector with U-tube , 2010 .

[27]  W. Beckman,et al.  Solar Engineering of Thermal Processes , 1985 .

[28]  Ruobing Liang,et al.  Theoretical and experimental investigation of the filled-type evacuated tube solar collector with U tube , 2011 .

[29]  Simon Furbo,et al.  Vertical evacuated tubular-collectors utilizing solar radiation from all directions , 2004 .