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Comparison of four heuristic regression techniques in solar radiation modeling: Kriging method vs RSM, MARS and M5 model tree

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  • Keshtegar, Behrooz
  • Mert, Cihan
  • Kisi, Ozgur

Abstract

In this study, four different heuristic regression methods including Kriging, response surface method (RSM), multivariate adaptive regression (MARS) and M5 model tree (M5Tree) have been investigated for accurate estimating of solar radiation with different input data. Monthly solar radiation (SR) from Adana and Antakya stations, which are located in Eastern Mediterranean Region of Turkey is estimated based on the input data of maximum temperature (Tmax), minimum temperature (Tmin), sunshine hours (Hs), wind speed (Ws), and relative humidity (RH). In Adana station, the best MARS model provided slightly better accuracy than the Kriging, RSM and M5Tree while the Kriging was found to be the better than the MARS, RSM and M5Tree in Antakya station. The predictions of M5Tree model are shown inaccurate results for both maximum errors and minimum agreement compared to another models. The effect of periodicity input is examined to obtain the accurate predictions of solar radiation for these stations based on the four heuristic –based modeling Kriging, MARS, RSM, M5Tree approaches. Periodicity input data improved the root mean square errors of the best MARS, RSM, M5Tree and Kriging models as 34%, 37%, 46% and 39% for Adana station and by 51%, 47%, 38% and 49% for Antakya station, respectively. The periodic Kriging models performed superior to the periodic MARS, RSM and M5Tree models.

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  • Keshtegar, Behrooz & Mert, Cihan & Kisi, Ozgur, 2018. "Comparison of four heuristic regression techniques in solar radiation modeling: Kriging method vs RSM, MARS and M5 model tree," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 330-341.
  • Handle: RePEc:eee:rensus:v:81:y:2018:i:p1:p:330-341
    DOI: 10.1016/j.rser.2017.07.054
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    1. Shamshirband, Shahaboddin & Mohammadi, Kasra & Khorasanizadeh, Hossein & Yee, Por Lip & Lee, Malrey & Petković, Dalibor & Zalnezhad, Erfan, 2016. "Estimating the diffuse solar radiation using a coupled support vector machine–wavelet transform model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 428-435.
    2. Ramedani, Zeynab & Omid, Mahmoud & Keyhani, Alireza & Shamshirband, Shahaboddin & Khoshnevisan, Benyamin, 2014. "Potential of radial basis function based support vector regression for global solar radiation prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 1005-1011.
    3. Krishna Singh & Mahesh Pal & V. Singh, 2010. "Estimation of Mean Annual Flood in Indian Catchments Using Backpropagation Neural Network and M5 Model Tree," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(10), pages 2007-2019, August.
    4. Behrooz Keshtegar & Mohammed Falah Allawi & Haitham Abdulmohsin Afan & Ahmed El-Shafie, 2016. "Optimized River Stream-Flow Forecasting Model Utilizing High-Order Response Surface Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(11), pages 3899-3914, September.
    5. Olatomiwa, Lanre & Mekhilef, Saad & Shamshirband, Shahaboddin & Petković, Dalibor, 2015. "Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1784-1791.
    6. Mohanty, Sthitapragyan & Patra, Prashanta Kumar & Sahoo, Sudhansu Sekhar, 2016. "Prediction and application of solar radiation with soft computing over traditional and conventional approach – A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 778-796.
    7. Besharat, Fariba & Dehghan, Ali A. & Faghih, Ahmad R., 2013. "Empirical models for estimating global solar radiation: A review and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 798-821.
    8. Kumar, Rajesh & Aggarwal, R.K. & Sharma, J.D., 2015. "Comparison of regression and artificial neural network models for estimation of global solar radiations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1294-1299.
    9. Shamshirband, Shahaboddin & Mohammadi, Kasra & Yee, Por Lip & Petković, Dalibor & Mostafaeipour, Ali, 2015. "A comparative evaluation for identifying the suitability of extreme learning machine to predict horizontal global solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1031-1042.
    10. Lanre Olatomiwa & Saad Mekhilef & Shahaboddin Shamshirband & Dalibor Petkovic, 2015. "Potential of support vector regression for solar radiation prediction in Nigeria," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 77(2), pages 1055-1068, June.
    11. Wang, Lunche & Kisi, Ozgur & Zounemat-Kermani, Mohammad & Salazar, Germán Ariel & Zhu, Zhongmin & Gong, Wei, 2016. "Solar radiation prediction using different techniques: model evaluation and comparison," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 384-397.
    12. Teke, Ahmet & Yıldırım, H. Başak & Çelik, Özgür, 2015. "Evaluation and performance comparison of different models for the estimation of solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1097-1107.
    13. Gairaa, Kacem & Khellaf, Abdallah & Messlem, Youcef & Chellali, Farouk, 2016. "Estimation of the daily global solar radiation based on Box–Jenkins and ANN models: A combined approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 238-249.
    14. Mohammadi, Kasra & Shamshirband, Shahaboddin & Petković, Dalibor & Khorasanizadeh, Hossein, 2016. "Determining the most important variables for diffuse solar radiation prediction using adaptive neuro-fuzzy methodology; case study: City of Kerman, Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1570-1579.
    15. Zhang, Jianyuan & Zhao, Li & Deng, Shuai & Xu, Weicong & Zhang, Ying, 2017. "A critical review of the models used to estimate solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 314-329.
    16. Ozgur Kisi, 2015. "Streamflow Forecasting and Estimation Using Least Square Support Vector Regression and Adaptive Neuro-Fuzzy Embedded Fuzzy c-means Clustering," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(14), pages 5109-5127, November.
    17. Yadav, Amit Kumar & Chandel, S.S., 2015. "Solar energy potential assessment of western Himalayan Indian state of Himachal Pradesh using J48 algorithm of WEKA in ANN based prediction model," Renewable Energy, Elsevier, vol. 75(C), pages 675-693.
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    Keywords

    Solar radiation; Kriging; RSM; MARS; M5 model tree; Modeling;
    All these keywords.

    JEL classification:

    • M5 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics

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