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Evaluating the effect of air pollution on global and diffuse solar radiation prediction using support vector machine modeling based on sunshine duration and air temperature

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  • Fan, Junliang
  • Wu, Lifeng
  • Zhang, Fucang
  • Cai, Huanjie
  • Wang, Xiukang
  • Lu, Xianghui
  • Xiang, Youzhen

Abstract

Increasing air pollutants attenuate surface solar radiation, and thus can be influential variables for solar radiation prediction. In this study, six air pollutants of PM2.5, PM10, SO2, NO2, CO and O3 as well as air quality index (AQI) were chosen for analyzing their single and integrated effects on daily global and diffuse solar radiation (Rs and Rd) prediction. Seven single air pollution parameters, 15 combinations of two parameters and 20 combinations of three parameters were considered using Support Vector Machine (SVM) based on sunshine duration or air temperature. Daily meteorological and air pollution data between January 2014 and December 2015 from China's capital city of Beijing were used to train SVM models and data from January 2016 to December 2016 for testing. Results show that AQI was the most relevant air pollution parameter for both Rs and Rd prediction, followed by O3 for Rs and by PM2.5 for Rd with slight difference as that of AQI. The combination of PM10 and O3 and the combination of PM2.5 and O3 were the most influential combination of two air pollution inputs for Rs and Rd prediction, respectively. The combination of PM2.5, PM10 and O3 was the most optimal combination of three air pollution inputs for both daily Rs and Rd prediction. Compared with SVM models without considering air pollution, the accuracy of SVM models with the most influential combinations of one, two and three air pollution inputs was improved by 13.9%, 19.8% and 22.2% in terms of RMSE for sunshine-based Rs, respectively. The corresponding values were 15.2%, 22.0% and 22.8% for temperature-based Rs, 16.1%, 21.5% and 24.5% for sunshine-based Rd, and 16.8%, 22.0% and 23.3% for temperature-based Rd. The results demonstrate the importance of appropriate selection of air pollution inputs to improve the accuracy of Rs and Rd prediction in air-polluted regions.

Suggested Citation

  • Fan, Junliang & Wu, Lifeng & Zhang, Fucang & Cai, Huanjie & Wang, Xiukang & Lu, Xianghui & Xiang, Youzhen, 2018. "Evaluating the effect of air pollution on global and diffuse solar radiation prediction using support vector machine modeling based on sunshine duration and air temperature," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 732-747.
    • Handle: RePEc:eee:rensus:v:94:y:2018:i:c:p:732-747
    DOI: 10.1016/j.rser.2018.06.029
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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. Achour, Lazhar & Bouharkat, Malek & Assas, Ouarda & Behar, Omar, 2017. "Hybrid model for estimating monthly global solar radiation for the Southern of Algeria: (Case study: Tamanrasset, Algeria)," Energy, Elsevier, vol. 135(C), pages 526-539.
    3. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    4. Jiang, Yingni, 2009. "Estimation of monthly mean daily diffuse radiation in China," Applied Energy, Elsevier, vol. 86(9), pages 1458-1464, September.
    5. 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.
    6. Jamil, Basharat & Akhtar, Naiem, 2017. "Comparison of empirical models to estimate monthly mean diffuse solar radiation from measured data: Case study for humid-subtropical climatic region of India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1326-1342.
    7. Hassan, Gasser E. & Youssef, M. Elsayed & Mohamed, Zahraa E. & Ali, Mohamed A. & Hanafy, Ahmed A., 2016. "New Temperature-based Models for Predicting Global Solar Radiation," Applied Energy, Elsevier, vol. 179(C), pages 437-450.
    8. 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.
    9. 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.
    10. Khodakarami, Jamal & Ghobadi, Parisa, 2016. "Urban pollution and solar radiation impacts," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 965-976.
    11. Jahani, Babak & Dinpashoh, Y. & Raisi Nafchi, Atefeh, 2017. "Evaluation and development of empirical models for estimating daily solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 878-891.
    12. Furlan, Claudia & de Oliveira, Amauri Pereira & Soares, Jacyra & Codato, Georgia & Escobedo, João Francisco, 2012. "The role of clouds in improving the regression model for hourly values of diffuse solar radiation," Applied Energy, Elsevier, vol. 92(C), pages 240-254.
    13. Bakirci, Kadir, 2009. "Models of solar radiation with hours of bright sunshine: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(9), pages 2580-2588, December.
    14. Zou, Ling & Wang, Lunche & Xia, Li & Lin, Aiwen & Hu, Bo & Zhu, Hongji, 2017. "Prediction and comparison of solar radiation using improved empirical models and Adaptive Neuro-Fuzzy Inference Systems," Renewable Energy, Elsevier, vol. 106(C), pages 343-353.
    15. Despotovic, Milan & Nedic, Vladimir & Despotovic, Danijela & Cvetanovic, Slobodan, 2015. "Review and statistical analysis of different global solar radiation sunshine models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1869-1880.
    16. Samuel Chukwujindu, Nwokolo, 2017. "A comprehensive review of empirical models for estimating global solar radiation in Africa," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 955-995.
    17. 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.
    18. Pandey, Chanchal Kumar & Katiyar, A.K., 2009. "A comparative study to estimate daily diffuse solar radiation over India," Energy, Elsevier, vol. 34(11), pages 1792-1796.
    19. Mohammad Mehdi Lotfinejad & Reza Hafezi & Majid Khanali & Seyed Sina Hosseini & Mehdi Mehrpooya & Shahaboddin Shamshirband, 2018. "A Comparative Assessment of Predicting Daily Solar Radiation Using Bat Neural Network (BNN), Generalized Regression Neural Network (GRNN), and Neuro-Fuzzy (NF) System: A Case Study," Energies, MDPI, vol. 11(5), pages 1-15, May.
    20. Bakirci, Kadir, 2015. "Models for the estimation of diffuse solar radiation for typical cities in Turkey," Energy, Elsevier, vol. 82(C), pages 827-838.
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