Characterization of Concentrating Photovoltaic modules by cooperative competitive Radial Basis Function Networks

Concentrating Photovoltaic (CPV) technology attempts to optimize the efficiency of solar energy production systems. As conventional Photovoltaic (PV) technology, suffers from variability in its production and needs models for determining the exact module performance. There are several problems when analyzing CPV systems performance with traditional techniques due to absence of standardization. In this sense it is remarkable the importance for the emerging CPV technology, of the existence of models which allow the prediction of modules performance from initial atmospheric conditions. In this paper, a CPV module is studied by means of atmospheric conditions obtained using an automatic test and measuring system developed by the authors. The characterization of the CPV module is carried out considering incident normal irradiance, ambient temperature, spectral irradiance distribution and wind speed. CO^2RBFN, a cooperative-competitive algorithm for the design of radial basis neural networks, is adapted and applied to these data obtaining a model with a good level of accuracy on test data, improving the results obtained by other methods considered in the experimental comparison. These results are promising and the obtained model could be used to work out the maximum power at the CPV reporting conditions and to analyze the performance of the module under any conditions and at any moment.

[1]  J. Rustagi.  Optimization Techniques in Statistics , 1994 .

[2]  Betul Bektas Ekici,et al.  Prediction of building energy needs in early stage of design by using ANFIS , 2011, Expert Syst. Appl..

[3]  Jooyoung Park,et al.  Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.

[4]  David J. Sheskin,et al.  Handbook of Parametric and Nonparametric Statistical Procedures , 1997 .

[5]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[6]  Bernhard Sick,et al.  Evolutionary optimization of radial basis function classifiers for data mining applications , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[7]  Jerry M. Mendel,et al.  Generating fuzzy rules by learning from examples , 1992, IEEE Trans. Syst. Man Cybern..

[8]  J. Nazuno.  Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[9]  Bernhard Schölkopf,et al.  New Support Vector Algorithms , 2000, Neural Computation.

[10]  F. Rubio,et al.  Comparison of the different CPV rating procedures: Real measurements in ISFOC , 2009, 2009 34th IEEE Photovoltaic Specialists Conference (PVSC).

[11]  S. Campbell,et al.  Sputtering of metal oxide tunnel junctions for tandem solar cells , 2013, 2013 IEEE 39th Photovoltaic Specialists Conference (PVSC).

[12]  Witold Pedrycz,et al.  Conditional fuzzy clustering in the design of radial basis function neural networks , 1998, IEEE Trans. Neural Networks.

[13]  Bruce A. Whitehead,et al.  Cooperative-competitive genetic evolution of radial basis function centers and widths for time series prediction , 1996, IEEE Trans. Neural Networks.

[14]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[15]  Thomas Bäck,et al.  Evolutionary computation: comments on the history and current state , 1997, IEEE Trans. Evol. Comput..

[16]  D. Sheskin.  Handbook of parametric and nonparametric statistical procedures, 2nd ed. , 2000 .

[17]  Antonio J. Rivera,et al.  CO2RBFN: an evolutionary cooperative–competitive RBFN design algorithm for classification problems , 2010, Soft Comput..

[18]  Ying-Pin Chang,et al.  An ant direction hybrid differential evolution algorithm in determining the tilt angle for photovoltaic modules , 2010, Expert Syst. Appl..

[19]  H. Takakura,et al.  Uniqueness verification of solar spectrum index of average photon energy for evaluating outdoor performance of photovoltaic modules , 2009 .

[20]  Consolación Gil,et al.  Evolutionary algorithms for the design of grid-connected PV-systems , 2012, Expert Syst. Appl..

[21]  Hsuan-Ming Feng,et al.  Evolutional RBFNs prediction systems generation in the applications of financial time series data , 2011, Expert Syst. Appl..

[22]  E. Izgi,et al.  Short–mid-term solar power prediction by using artificial neural networks , 2012 .

[23]  Gerald Siefer,et al.  A method for using CPV modules as temperature sensors and its application to rating procedures , 2011 .

[24]  D. Broomhead,et al.  Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .

[25]  Cengiz Kahraman,et al.  A fuzzy multicriteria methodology for selection among energy alternatives , 2010, Expert Syst. Appl..

[26]  Thomas R. Betts,et al.  Modelling long-term module performance based on realistic reporting conditions with consideration to spectral effects , 2003, 3rd World Conference onPhotovoltaic Energy Conversion, 2003. Proceedings of.

[27]  Christian W. Dawson,et al.  A review of genetic algorithms applied to training radial basis function networks , 2004, Neural Computing & Applications.

[28]  Yu-Choung Chang,et al.  A novel neural network with simple learning algorithm for islanding phenomenon detection of photovoltaic systems , 2011, Expert Syst. Appl..

[29]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

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

[31]  Matthew Muller,et al.  Minimizing Variation In Outdoor CPV Power Ratings , 2011 .

[32]  María José del Jesús,et al.  KEEL: a software tool to assess evolutionary algorithms for data mining problems , 2008, Soft Comput..

[33]  Jorge Aguilera,et al.  A new approach for sizing stand alone photovoltaic systems based in neural networks , 2005 .

[34]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

[35]  Gene H. Golub,et al.  Matrix computations , 1983 .

[36]  Daswin De Silva,et al.  A Data Mining Framework for Electricity Consumption Analysis From Meter Data , 2011, IEEE Transactions on Industrial Informatics.

[37]  Jorge Aguilera,et al.  An application of the multilayer perceptron: Solar radiation maps in Spain , 2005 .

[38]  L. Suganthi,et al.  Energy models for demand forecasting—A review , 2012 .

[39]  Bernard Widrow,et al.  30 years of adaptive neural networks: perceptron, Madaline, and backpropagation , 1990, Proc. IEEE.

[40]  Martin A. Green,et al.  Solar cell efficiency tables (Version 38) , 2011 .

[41]  Emilio Olias,et al.  Overview of the photovoltaic technology status and perspective in Spain , 2009 .

[42]  Francisco Herrera,et al.  A study on the use of statistical tests for experimentation with neural networks: Analysis of parametric test conditions and non-parametric tests , 2007, Expert Syst. Appl..

[43]  Hortensia Amaris,et al.  Integration of renewable energy sources in smart grids by means of evolutionary optimization algorithms , 2012, Expert Syst. Appl..

[44]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[45]  F. Rubio,et al.  Field tests on CPV ISFOC plants , 2009, Optics + Photonics for Sustainable Energy.

[46]  M. Drif,et al.  A new estimation method of irradiance on a partially shaded PV generator in grid-connected photovoltaic systems , 2008 .

[47]  Ignacio Rojas,et al.  A new hybrid methodology for cooperative-coevolutionary optimization of radial basis function networks , 2007, Soft Comput..

[48]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[49]  Jian Wang,et al.  Short, medium and long term load forecasting model and virtual load forecaster based on radial basis function neural networks , 2010 .

[50]  Ebrahim H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Hum. Comput. Stud..

[51]  Kenneth A. De Jong,et al.  Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents , 2000, Evolutionary Computation.

[52]  Timothy Masters,et al.  Multilayer Feedforward Networks , 1993 .

[53]  Wei Qiao,et al.  Short-term solar power prediction using an RBF neural network , 2011, 2011 IEEE Power and Energy Society General Meeting.

[54]  Gene H. Golub,et al.  Matrix computations (3rd ed.) , 1996 .

[55]  Pedro Pérez-Higueras,et al.  CPV standardization: An overview , 2010 .

[56]  John C. Platt.  A Resource-Allocating Network for Function Interpolation , 1991, Neural Computation.