Research Article
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Application of support vector regression integrated with firefly optimization algorithm for predicting global solar radiation

Year 2018, Volume: 2 Issue: 4, 180 - 189, 31.12.2018
https://doi.org/10.30521/jes.458328

Abstract

A fundamental factor
for proficient designing of solar energy systems is providing precise
estimations of the solar radiation. Global solar radiation (GSR) is a vital
parameter for designing and operating solar energy systems. Because records of
GSR are not available in many places, especially in developing countries, this
research aims to model the GSR using support vector regression (SVR) in a
hybrid manner that is integrated with the firefly Optimization algorithm
(SVR-FFA). For this purpose, the daily meteorological parameters and GSR
measured from beginning of 2011 to the end of 2013 at Tabriz synoptic station
were utilized. For assessing the performance of the mentioned methods,
different statistical indicators were implemented. For all of the defined
predictive models with different combinations of meteorological parameters, the
performance of the SVR-FFA hybrid model is better than the classical SVR,
evidenced by the higher value of R (~0892-0.982 relative to ~0.891-0.977) and
lower values of RMSE and MAE (~1.551-3.725vs.1.748-4.067 and
~0.911-2.862vs.1.103-2.742). As a remarkable point studied empirical equations
had higher prediction errors comparing with the developed SVR-FFA models.
Conclusively, the obtained results proved the high proficiencies of SVR-FFA
method for predicting global solar radiation.

References

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  • Ramedani Z,Omid M,Keyhani A,Shamshirband S,Khoshne visan B. Potential of radial basis function based support vector regression for global solar radiation prediction.Renewable Sustainable Energy Rev2014;39(1):1005–11.
  • Guzmán, SM, Paz, JO, Tagert, MLM, Mercer, AE, Pote, JW. An integrated SVR and crop model to estimate the impacts of irrigation on daily groundwater levels. Agricultural Systems 2018; 159: 248-259.
  • Yang, XS. Firefly algorithm, stochastic test functions and design optimization. Journal Bio-Inspired Computation 2010; 2(2): 78–84.
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  • Bristow, KL, Campbell, GS. On the relationship between incoming solar radiation and daily maximum and minimum temperature. Agricultural and Forest Meteorology. 1984; 31: 159-166.
  • Elagib, N, Mansell, MG. New approaches for estimating global solar radiation across Sudan. Energy Conversion and Management 2000; 41: 419-434.
  • Chen, RS, Ersi, K, Yang, JP, Lu, SH, Zhao, WZ. Validation of five global radiation models with measured daily data in China. Energy Conversion and Management 2004; 45: 1759-1769.
  • Samadianfard, S, Sattari, MT, Kisi, O, Kazemi, H. Determining flow friction factor in irrigation pipes using data mining and artificial intelligence approaches. Applied Artificial Intelligence 2014; 28: 793-813.
  • Kurup, PU, Dudani, NK. Neural networks for profiling stress history of clays from PCPT data. Journal of Geotechnical and Geoenvironmental Engineering 2014; 128(7): 569-579.
  • Pal, M. Support vector machines-based modelling of seismic liquefaction potential. International Journal for Numerical and Analytical Methods in Geomechanics 2006; 30(10): 983-996.
  • Samadianfard, S, Delirhasannia, R, Kisi, O, Agirre-Basurko, E. Comparative analysis of ozone level prediction models using gene expression programming and multiple linear regression. GEOFIZIKA 2013, 30, 43-74.
Year 2018, Volume: 2 Issue: 4, 180 - 189, 31.12.2018
https://doi.org/10.30521/jes.458328

Abstract

References

  • Akikur, R, Saidur R, Ping, H, Ullah, K. Comparative study of stand-aloneand hybrid solar energy systems suitable for off-grid rural electrification: a review. Renewable Sustainable Energy Reviews 2013; 27(3):738-52.
  • Kalogirou, SA. Artificial neural networks and genetic algorithms for the modeling, simulation and performance prediction of solar energy systems. Assessment and Simulation Tools for Sustainable Energy Systems. Green Energy and Technology 2013; 225–245.
  • Fadare, DA. Modelling of solar energy potential in Nigeria using an artificial neural network model. Applied Energy 2009; 86:1410–22.
  • Celik, E, Gor, H, Ozturk, N, Kurt, E. Application of artificial neural network to estimate power generation and efficiency of a new axial flux permanent magnet synchronous generator. International Journal of Hydrogen Energy, 2017; 42(28): 17692-17699.
  • Celik, E, Uzun, Y, Kurt, E, Ozturk, N, Topaloglu, N. A neural network design for the estimation of nonlinear behavior of a magnetically-excited piezoelectric harvester. Journal of Electronic Materials, 2018; 47(8): 4412-4420.
  • Celik, E, Cavusoglu, O, Gurun, H, E, Ozturk, N, Topaloglu, N. Estimation of the clearance effect in the blanking process of CuZn30 sheet metal using neural network−A comparative study. Bilişim Teknolojileri Dergisi, 2018; 11(2), 187-193.
  • Ornella, L, Tapia, E. Supervised machine learning and heterotic classification of maize (Zea mays L.) using molecular marker data. Computers and Electronics in Agriculture 2010; 74: 250–257.
  • Ananthakrishnan S, Prasad R, Stallard D, Natarajan P. Batch-mode semisupervised active learning for statistical machine translation, Comput. Speech Lang.2013; 17: 397–406.
  • Wei Z, Tao T, ZhuoShu D, Zio E, A dynamic particle filter-support vector regression method for reliability prediction, Reliab. Eng. Syst. Safe. 2013; 11(9): 109–116.
  • Chen, JL, Liu, HB, Wu, W, Xie, DT. Estimation of monthly solar radiation from measured temperatures using support vector machines-a case study. Renewable Energy 2011; 36: 413–420.
  • Zeng, J, Qiao, W. Short-term solar power prediction using a support vector machine. Renewable Energy 2013; 52:118–127.
  • Ekici, BB. A least squares support vector machine model for prediction of the next day solar insolation for effective use of PV systems. Measurement 2014; 50: 255–262.
  • Solmaz, O, Ozgoren, M. Prediction of hourly solar radiationin six provinces in turkey by artificial neural networks. Journal of Energy Engineering 2012; 138, 194–204.
  • Jiang, Y. Computation of monthly mean daily global solar radiation in China using artificial neural networks and comparison with other empirical models. Energy 2009; 1276–1283.
  • Benghanem, M, Mellit, A, Alamri, S. ANN-based modelling and estimation of daily global solar radiation data: acasestudy. Energy Conversion and Management 2009; 50(7):1644–55.
  • Mellit, A, Hadj, Arab, A, Khorissi, N, Salhi, H. An ANFIS-based forecasting for solar radiation data from sunshine duration and ambient temperature. In: IEEE power engineering society general meeting; 24–28 June 2007, Florida (USA). p. 1–6
  • Bosch JL, Lopez G, Batllesa FJ. Daily solar irradiation estimation over a mountainous area using artificial neural networks. Renew Energy 2008;33(7):1622–8.
  • Ramedani Z,Omid M,Keyhani A,Shamshirband S,Khoshne visan B. Potential of radial basis function based support vector regression for global solar radiation prediction.Renewable Sustainable Energy Rev2014;39(1):1005–11.
  • Guzmán, SM, Paz, JO, Tagert, MLM, Mercer, AE, Pote, JW. An integrated SVR and crop model to estimate the impacts of irrigation on daily groundwater levels. Agricultural Systems 2018; 159: 248-259.
  • Yang, XS. Firefly algorithm, stochastic test functions and design optimization. Journal Bio-Inspired Computation 2010; 2(2): 78–84.
  • Kayarvizhy, N, Kanmani, S, Uthariaraj R. ANN models optimized using swarm intelligence algorithms. WSEAS Transactions on Computers 2014; 13: 501-519.
  • URL1 < https://www.google.com/maps/@34.3808769,50.7895208,2502254m/data=!3m1!1e3?hl=en>
  • Angstrom, A. Solar and terrestrial radiation. Quarterly Journal of the Royal Meteorological Society 1924; 50: 121-125.
  • Prescott, JA. Evaporation from water surface in relation to solar radiation. Transactions of the Royal Society of South Australia. 1940; 64: 114–125.
  • Bristow, KL, Campbell, GS. On the relationship between incoming solar radiation and daily maximum and minimum temperature. Agricultural and Forest Meteorology. 1984; 31: 159-166.
  • Elagib, N, Mansell, MG. New approaches for estimating global solar radiation across Sudan. Energy Conversion and Management 2000; 41: 419-434.
  • Chen, RS, Ersi, K, Yang, JP, Lu, SH, Zhao, WZ. Validation of five global radiation models with measured daily data in China. Energy Conversion and Management 2004; 45: 1759-1769.
  • Samadianfard, S, Sattari, MT, Kisi, O, Kazemi, H. Determining flow friction factor in irrigation pipes using data mining and artificial intelligence approaches. Applied Artificial Intelligence 2014; 28: 793-813.
  • Kurup, PU, Dudani, NK. Neural networks for profiling stress history of clays from PCPT data. Journal of Geotechnical and Geoenvironmental Engineering 2014; 128(7): 569-579.
  • Pal, M. Support vector machines-based modelling of seismic liquefaction potential. International Journal for Numerical and Analytical Methods in Geomechanics 2006; 30(10): 983-996.
  • Samadianfard, S, Delirhasannia, R, Kisi, O, Agirre-Basurko, E. Comparative analysis of ozone level prediction models using gene expression programming and multiple linear regression. GEOFIZIKA 2013, 30, 43-74.
There are 31 citations in total.

Details

Primary Language English
Journal Section Research Articles
Authors

Saeed Samadianfard 0000-0002-6876-7182

Salar Jarhan This is me 0000-0002-3432-9985

Hamed Sadri Nahand This is me 0000-0002-7091-505X

Publication Date December 31, 2018
Acceptance Date November 13, 2018
Published in Issue Year 2018 Volume: 2 Issue: 4

Cite

Vancouver Samadianfard S, Jarhan S, Sadri Nahand H. Application of support vector regression integrated with firefly optimization algorithm for predicting global solar radiation. Journal of Energy Systems. 2018;2(4):180-9.

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