A hybrid SVM-FFA method for prediction of monthly mean global solar radiation

In this study, a hybrid support vector machine–firefly optimization algorithm (SVM-FFA) model is proposed to estimate monthly mean horizontal global solar radiation (HGSR). The merit of SVM-FFA is assessed statistically by comparing its performance with three previously used approaches. Using each approach and long-term measured HGSR, three models are calibrated by considering different sets of meteorological parameters measured for Bandar Abbass situated in Iran. It is found that the model (3) utilizing the combination of relative sunshine duration, difference between maximum and minimum temperatures, relative humidity, water vapor pressure, average temperature, and extraterrestrial solar radiation shows superior performance based upon all approaches. Moreover, the extraterrestrial radiation is introduced as a significant parameter to accurately estimate the global solar radiation. The survey results reveal that the developed SVM-FFA approach is greatly capable to provide favorable predictions with significantly higher precision than other examined techniques. For the SVM-FFA (3), the statistical indicators of mean absolute percentage error (MAPE), root mean square error (RMSE), relative root mean square error (RRMSE), and coefficient of determination (R2) are 3.3252 %, 0.1859 kWh/m2, 3.7350 %, and 0.9737, respectively which according to the RRMSE has an excellent performance. As a more evaluation of SVM-FFA (3), the ratio of estimated to measured values is computed and found that 47 out of 48 months considered as testing data fall between 0.90 and 1.10. Also, by performing a further verification, it is concluded that SVM-FFA (3) offers absolute superiority over the empirical models using relatively similar input parameters. In a nutshell, the hybrid SVM-FFA approach would be considered highly efficient to estimate the HGSR.

[1]  A.H. Sung,et al.  Identifying important features for intrusion detection using support vector machines and neural networks , 2003, 2003 Symposium on Applications and the Internet, 2003. Proceedings..

[2]  J. Mubiru,et al.  Assessing the performance of global solar radiation empirical formulations in Kampala, Uganda , 2007 .

[3]  Mac McKee,et al.  Multi-time scale stream flow predictions: The support vector machines approach , 2006 .

[4]  David Pozo-Vázquez,et al.  An artificial neural network ensemble model for estimating global solar radiation from Meteosat satellite images , 2013 .

[5]  Mario Kusek,et al.  A self-optimizing mobile network: Auto-tuning the network with firefly-synchronized agents , 2012, Inf. Sci..

[6]  Abdullah Gani,et al.  Wind turbine power coefficient estimation by soft computing methodologies: Comparative study , 2014 .

[7]  Majid Jamil,et al.  Fuzzy logic based modeling and estimation of global solar energy using meteorological parameters , 2014 .

[8]  H. Karam,et al.  Estimation of atmospheric turbidity and surface radiative parameters using broadband clear sky solar irradiance models in Rio de Janeiro-Brasil , 2016, Theoretical and Applied Climatology.

[9]  Jamshid Piri,et al.  Comparison of LLR, MLP, Elman, NNARX and ANFIS Models―with a case study in solar radiation estimation , 2009 .

[10]  B. Rudolf,et al.  World Map of the Köppen-Geiger climate classification updated , 2006 .

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

[12]  A. Mellit,et al.  A simplified calibrated model for estimating daily global solar radiation in Madinah, Saudi Arabia , 2014, Theoretical and Applied Climatology.

[13]  De-ti Xie,et al.  Estimation of monthly solar radiation from measured temperatures using support vector machines – A case study , 2011 .

[14]  T.-L. Lee,et al.  Support vector regression methodology for storm surge predictions , 2008 .

[15]  Ranz,et al.  World Map of the Köppen-Geiger climate classification updated — Source link , 2006 .

[16]  Gilles Notton,et al.  Estimation of 5-min time-step data of tilted solar global irradiation using ANN (Artificial Neural Network) model , 2014 .

[17]  Sancho Salcedo-Sanz,et al.  Daily global solar radiation prediction based on a hybrid Coral Reefs Optimization – Extreme Learning Machine approach , 2014 .

[18]  L. S. Davis,et al.  An assessment of support vector machines for land cover classi(cid:142) cation , 2002 .

[19]  Bijaya K. Panigrahi,et al.  A Support Vector Machine-Firefly Algorithm based forecasting model to determine malaria transmission , 2014, Neurocomputing.

[20]  Hongbin Liu,et al.  General models for estimating daily global solar radiation for different solar radiation zones in mainland China , 2013 .

[21]  L. Mora-López,et al.  Multiplicative ARMA models to generate hourly series of global irradiation , 1998 .

[22]  Amit Kumar Yadav,et al.  Solar radiation prediction using Artificial Neural Network techniques: A review , 2014 .

[23]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[24]  William A. Beckman,et al.  Solar Engineering of Thermal Processes, 2nd ed. , 1994 .

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

[26]  M. Russo,et al.  Genetic programming for photovoltaic plant output forecasting , 2014 .

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

[28]  Rolf T. Wigand,et al.  Community Detection in Complex Networks: Multi-objective Enhanced Firefly Algorithm , 2013, Knowl. Based Syst..

[29]  Wei-Zhen Lu,et al.  Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends. , 2005, Chemosphere.

[30]  Sara Saeidi Ramiyani,et al.  A hybrid computational approach to estimate solar global radiation: An empirical evidence from Iran , 2013 .

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

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

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

[34]  Xin-She Yang,et al.  Firefly algorithm, stochastic test functions and design optimisation , 2010, Int. J. Bio Inspired Comput..

[35]  Yousef A.G. Abdalla,et al.  New Correlations of Global Solar Radiation with Meteorological Parameters for Bahrain , 1994 .

[36]  N. Poursalehi,et al.  Continuous firefly algorithm applied to PWR core pattern enhancement , 2013 .

[37]  Cyril Voyant,et al.  Numerical Weather Prediction (NWP) and hybrid ARMA/ANN model to predict global radiation , 2012, ArXiv.

[38]  Michael R. Lyu,et al.  Localized support vector regression for time series prediction , 2009, Neurocomputing.

[39]  C. K. Chan,et al.  Prediction of hourly solar radiation using a novel hybrid model of ARMA and TDNN , 2011 .

[40]  Mat Kiah M.L.,et al.  Wind turbine power coefficient estimation by soft computing methodologies: Comparative study , 2014 .

[41]  S. C. Kaushik,et al.  Assessment of diffuse solar energy under general sky condition using artificial neural network , 2009 .

[42]  Angelika Bayer,et al.  Solar Engineering Of Thermal Processes , 2016 .

[43]  John Boland,et al.  Forecasting solar radiation on an hourly time scale using a Coupled AutoRegressive and Dynamical System (CARDS) model , 2013 .

[44]  Taher Niknam,et al.  An Adaptive Modified Firefly Optimisation Algorithm based on Hong's Point Estimate Method to optimal operation management in a microgrid with consideration of uncertainties , 2013 .

[45]  Andrew H. Sung,et al.  Intrusion detection using neural networks and support vector machines , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[46]  C. W. Tong,et al.  A new hybrid support vector machine–wavelet transform approach for estimation of horizontal global solar radiation , 2015 .

[47]  F. A. Ogbu,et al.  The importance of maximum air temperature in the parameterisation of solar radiation in Nigeria , 1995 .

[48]  Christian A. Gueymard,et al.  A review of validation methodologies and statistical performance indicators for modeled solar radiation data: Towards a better bankability of solar projects , 2014 .

[49]  Ming Li,et al.  Estimating daily global solar radiation during the growing season in Northeast China using the Ångström–Prescott model , 2012, Theoretical and Applied Climatology.

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

[51]  Dawei Han,et al.  A hybrid modelling approach for assessing solar radiation , 2015, Theoretical and Applied Climatology.

[52]  T. McMahon,et al.  Updated world map of the Köppen-Geiger climate classification , 2007 .

[53]  Turan Paksoy,et al.  A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey , 2012 .

[54]  Shiliang Sun,et al.  Multitask multiclass support vector machines: Model and experiments , 2013, Pattern Recognit..

[55]  Jilong Chen,et al.  Evaluation of support vector machine for estimation of solar radiation from measured meteorological variables , 2014, Theoretical and Applied Climatology.

[56]  O. S. Sastry,et al.  Estimation of solar radiation using a combination of Hidden Markov Model and generalized Fuzzy model , 2013 .

[57]  Samy Bengio,et al.  SVMTorch: Support Vector Machines for Large-Scale Regression Problems , 2001, J. Mach. Learn. Res..

[58]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[59]  Dalibor Petković,et al.  Potential of adaptive neuro-fuzzy system for prediction of daily global solar radiation by day of the year , 2015 .

[60]  Yu Zhang,et al.  Prediction of solar radiation with genetic approach combing multi-model framework , 2014 .

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

[62]  Shiliang Sun,et al.  A survey of multi-view machine learning , 2013, Neural Computing and Applications.

[63]  J. Mubiru,et al.  Performance of empirical correlations for predicting monthly mean daily diffuse solar radiation values at Kampala, Uganda , 2007 .

[64]  Sheng-De Wang,et al.  Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space , 2009, Pattern Recognit..

[65]  Muammer Ozgoren,et al.  Estimation of global solar radiation using ANN over Turkey , 2012, Expert Syst. Appl..

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

[67]  T. A. Sharif,et al.  Estimation of monthly average solar radiation in Libya , 2006 .

[68]  Andrew H. Sung,et al.  Monitoring System Security Using Neural Networks and Support Vector Machines , 2001, HIS.

[69]  Shan Suthaharan,et al.  Support Vector Machine , 2016 .