A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation

Accurate estimation of the amount of horizontal global solar radiation for a particular field is an important input for decision processes in solar radiation investments. In this article, we focus on the estimation of yearly mean daily horizontal global solar radiation by using an approach that utilizes fuzzy regression functions with support vector machine (FRF-SVM). This approach is not seriously affected by outlier observations and does not suffer from the over-fitting problem. To demonstrate the utility of the FRF-SVM approach in the estimation of horizontal global solar radiation, we conduct an empirical study over a dataset collected in Turkey and applied the FRF-SVM approach with several kernel functions. Then, we compare the estimation accuracy of the FRF-SVM approach to an adaptive neuro-fuzzy system and a coplot supported-genetic programming approach. We observe that the FRF-SVM approach with a Gaussian kernel function is not affected by both outliers and over-fitting problem and gives the most accurate estimates of horizontal global solar radiation among the applied approaches. Consequently, the use of hybrid fuzzy functions and support vector machine approaches is found beneficial in long-term forecasting of horizontal global solar radiation over a region with complex climatic and terrestrial characteristics.

[1]  Fatih Evrendilek,et al.  Assessing solar radiation models using multiple variables over Turkey , 2008 .

[2]  Khubaib Amjad Alam,et al.  Support vector regression based prediction of global solar radiation on a horizontal surface , 2015 .

[3]  Shervin Motamedi,et al.  A hybrid SVM-FFA method for prediction of monthly mean global solar radiation , 2015, Theoretical and Applied Climatology.

[4]  O. Şenkal.  Modeling of solar radiation using remote sensing and artificial neural network in Turkey , 2010 .

[5]  Gerardo Beni,et al.  A Validity Measure for Fuzzy Clustering , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  E. Arcaklioğlu,et al.  Use of artificial neural networks for mapping of solar potential in Turkey , 2004 .

[7]  Ozgur Kisi,et al.  Modeling solar radiation of Mediterranean region in Turkey by using fuzzy genetic approach , 2014 .

[8]  Alexander J. Smola,et al.  Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.

[9]  I. Burhan Türksen,et al.  Fuzzy functions with LSE , 2008, Appl. Soft Comput..

[10]  Saptarshi Das,et al.  Global solar irradiation prediction using a multi-gene genetic programming approach , 2013, ArXiv.

[11]  Cyril Voyant,et al.  Bayesian rules and stochastic models for high accuracy prediction of solar radiation , 2013, ArXiv.

[12]  A. Zeroual,et al.  Prediction of daily global solar radiation using fuzzy systems , 2007 .

[13]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[14]  A. Kamsin,et al.  Identifying the most significant input parameters for predicting global solar radiation using an ANFIS selection procedure , 2016 .

[15]  Shahaboddin Shamshirband,et al.  Temperature-based estimation of global solar radiation using soft computing methodologies , 2015, Theoretical and Applied Climatology.

[16]  Haydar Demirhan,et al.  The problem of multicollinearity in horizontal solar radiation estimation models and a new model for Turkey , 2014 .

[17]  H. Demirhan,et al.  New horizontal global solar radiation estimation models for Turkey based on robust coplot supported genetic programming technique , 2015 .

[18]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[19]  Por Lip Yee,et al.  Estimating the diffuse solar radiation using a coupled support vector machine–wavelet transform model , 2016 .

[20]  Badia Amrouche,et al.  Artificial neural network based daily local forecasting for global solar radiation , 2014 .

[21]  Shahaboddin Shamshirband,et al.  Potential of radial basis function based support vector regression for global solar radiation prediction , 2014 .

[22]  Saad Mekhilef,et al.  Global Solar Radiation Forecasting Based on SVM-Wavelet Transform Algorithm , 2016 .

[23]  Bernard Bourges,et al.  The European Solar Radiation Atlas Vol.1: Fundamentals and maps , 2000 .

[24]  Ozan Şenkal,et al.  The Estimation of Solar Radiation for Different Time Periods , 2010 .

[25]  Manuel Zarzo,et al.  Modeling the variability of solar radiation data among weather stations by means of principal components analysis , 2011 .

[26]  E. Tulcan-Paulescu,et al.  Fuzzy modelling of solar irradiation using air temperature data , 2008 .

[27]  Adnan Sözen,et al.  Solar-energy potential in Turkey , 2005 .

[28]  Hoay Beng Gooi,et al.  Solar radiation forecast based on fuzzy logic and neural networks , 2013 .

[29]  I. Turksen,et al.  Comparison of Fuzzy Functions with Fuzzy Rule Base Approaches , 2006 .

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

[31]  I. Burhan Türksen,et al.  Fuzzy functions with support vector machines , 2007, Inf. Sci..

[32]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[33]  W. Chow,et al.  Solar radiation model , 2001 .

[34]  Huashan Li,et al.  Estimating daily global solar radiation by day of year in China , 2010 .

[35]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[36]  Ersan Kabalci.  Development of a feasibility prediction tool for solar power plant installation analyses , 2011 .

[37]  I. Burhan Türksen,et al.  Uncertainty Modeling of Improved Fuzzy Functions With Evolutionary Systems , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[38]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[39]  Eleni Kaplani,et al.  Stochastic prediction of hourly global solar radiation for Patra, Greece , 2010 .

[40]  O. Şenkal,et al.  Estimation of solar radiation over Turkey using artificial neural network and satellite data , 2009 .

[41]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[42]  I. Burhan Türksen,et al.  Enhanced Fuzzy System Models With Improved Fuzzy Clustering Algorithm , 2008, IEEE Transactions on Fuzzy Systems.

[43]  James C. Bezdek,et al.  On cluster validity for the fuzzy c-means model , 1995, IEEE Trans. Fuzzy Syst..

[44]  Yasemin Kayhan Atilgan.  Robust Coplot Analysis , 2016, Commun. Stat. Simul. Comput..

[45]  Adnan Sözen,et al.  Estimation of solar potential in Turkey by artificial neural networks using meteorological and geographical data , 2004 .

[46]  Frank Klawonn,et al.  A contribution to convergence theory of fuzzy c-means and derivatives , 2003, IEEE Trans. Fuzzy Syst..

[47]  Minho Kim,et al.  New indices for cluster validity assessment , 2005, Pattern Recognit. Lett..

[48]  R.J. Hathaway,et al.  Switching regression models and fuzzy clustering , 1993, IEEE Trans. Fuzzy Syst..

[49]  I. Burhan Türksen,et al.  Modeling Uncertainty with Fuzzy Logic - With Recent Theory and Applications , 2009, Studies in Fuzziness and Soft Computing.

[50]  X. Wen,et al.  A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset , 2016 .

[51]  Haydar Demirhan,et al.  Statistical comparison of global solar radiation estimation models over Turkey , 2013 .

[52]  W. A. Rahoma,et al.  Application of Neuro-Fuzzy Techniques for Solar Radiation , 2011 .

[53]  ADNAN SOZEN,et al.  A Study for Estimating Solar Resources in Turkey Using Artificial Neural Networks , 2004 .

[54]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[55]  Kasra Mohammadi,et al.  A support vector machine–firefly algorithm-based model for global solar radiation prediction , 2015 .

[56]  G. Mihalakakou,et al.  Modeling the Global Solar Radiation on the Earth's Surface Using Atmospheric Deterministic and Intelligent Data-Driven Techniques , 1999 .

[57]  Marta Benito,et al.  Models for obtaining daily global solar radiation with measured air temperature data in Madrid (Spain) , 2011 .

[58]  Zne-Jung Lee,et al.  Hybrid robust support vector machines for regression with outliers , 2011, Appl. Soft Comput..

[59]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[60]  Mahmoud Omid,et al.  A comparative study between fuzzy linear regression and support vector regression for global solar radiation prediction in Iran , 2014 .

[61]  Saad Mekhilef,et al.  Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria , 2015 .

[62]  Wei Liu,et al.  Fighting global warming by climate engineering: Is the Earth radiation management and the solar radiation management any option for fighting climate change? , 2014 .