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Estimation of global solar radiation for the tropical wet climatic region of India: A theory of experimentation approach

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  • Makade, Rahul G.
  • Chakrabarti, Siddharth
  • Jamil, Basharat
  • Sakhale, C.N.

Abstract

The primary objective of the present work is to introduce a new method, i.e., Theory of Experimentation for prediction of monthly average global solar radiation. Meteorological data for 15 years is accessed considering six input predictors (i.e., latitude, longitude, altitude, relative humidity, temperature, and sunshine hours). Global solar radiation model is developed using various input parameters, and the accuracy of the developed models is assessed using statistical errors. The established model forms are also compared with the models available in the literature. Also, Global Performance Indicator is employed to sort the models for the development of the ranking system. A five-variable global solar radiation model (M-06) is found the best amongst all the proposed models (on training dataset) where the determination coefficient is 0.9424, and the mean percentage error is −0.1524%; whereas, for validation dataset, a two-variable regression model was seen to be the best. The study reveals that the effectiveness of the developed Global solar radiation model does not increase with an increase in the input variables; however, altitude, relative humidity, and sunshine hours are the dominating parameter. The proposed method exhibits a high potential of use in the prediction of monthly average global solar radiation.

Suggested Citation

  • Makade, Rahul G. & Chakrabarti, Siddharth & Jamil, Basharat & Sakhale, C.N., 2020. "Estimation of global solar radiation for the tropical wet climatic region of India: A theory of experimentation approach," Renewable Energy, Elsevier, vol. 146(C), pages 2044-2059.
  • Handle: RePEc:eee:renene:v:146:y:2020:i:c:p:2044-2059
    DOI: 10.1016/j.renene.2019.08.054
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    References listed on IDEAS

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    1. Jamil, Basharat & Akhtar, Naiem, 2017. "Estimation of diffuse solar radiation in humid-subtropical climatic region of India: Comparison of diffuse fraction and diffusion coefficient models," Energy, Elsevier, vol. 131(C), pages 149-164.
    2. 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.
    3. Paulescu, M. & Stefu, N. & Calinoiu, D. & Paulescu, E. & Pop, N. & Boata, R. & Mares, O., 2016. "Ångström–Prescott equation: Physical basis, empirical models and sensitivity analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 495-506.
    4. 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.
    5. Kumar, Anil & Prakash, Om & Dube, Akarshi, 2017. "A review on progress of concentrated solar power in India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 304-307.
    6. Benali, L. & Notton, G. & Fouilloy, A. & Voyant, C. & Dizene, R., 2019. "Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components," Renewable Energy, Elsevier, vol. 132(C), pages 871-884.
    7. Kumar, Anil & Kumar, Nitin & Baredar, Prashant & Shukla, Ashish, 2015. "A review on biomass energy resources, potential, conversion and policy in India," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 530-539.
    8. Senkal, Ozan & Kuleli, Tuncay, 2009. "Estimation of solar radiation over Turkey using artificial neural network and satellite data," Applied Energy, Elsevier, vol. 86(7-8), pages 1222-1228, July.
    9. Kaba, Kazım & Sarıgül, Mehmet & Avcı, Mutlu & Kandırmaz, H. Mustafa, 2018. "Estimation of daily global solar radiation using deep learning model," Energy, Elsevier, vol. 162(C), pages 126-135.
    10. 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.
    11. Chaturvedi, Vaibhav & Eom, Jiyong & Clarke, Leon E. & Shukla, Priyadarshi R., 2014. "Long term building energy demand for India: Disaggregating end use energy services in an integrated assessment modeling framework," Energy Policy, Elsevier, vol. 64(C), pages 226-242.
    12. Rathore, Pushpendra Kumar Singh & Rathore, Shailendra & Pratap Singh, Rudra & Agnihotri, Sugandha, 2018. "Solar power utility sector in india: Challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2703-2713.
    13. Almorox, J. & Benito, M. & Hontoria, C., 2005. "Estimation of monthly Angström–Prescott equation coefficients from measured daily data in Toledo, Spain," Renewable Energy, Elsevier, vol. 30(6), pages 931-936.
    14. Rehman, Shafiqur & Mohandes, Mohamed, 2008. "Artificial neural network estimation of global solar radiation using air temperature and relative humidity," Energy Policy, Elsevier, vol. 36(2), pages 571-576, February.
    15. Veeran, P.K. & Kumar, S., 1993. "Analysis of monthly average daily global radiation and monthly average sunshine duration at two tropical locations," Renewable Energy, Elsevier, vol. 3(8), pages 935-939.
    16. 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.
    17. Bayrakçı, Hilmi Cenk & Demircan, Cihan & Keçebaş, Ali, 2018. "The development of empirical models for estimating global solar radiation on horizontal surface: A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2771-2782.
    18. Pillai, Indu R. & Banerjee, Rangan, 2009. "Renewable energy in India: Status and potential," Energy, Elsevier, vol. 34(8), pages 970-980.
    19. Kumar, Ravinder & Umanand, L., 2005. "Estimation of global radiation using clearness index model for sizing photovoltaic system," Renewable Energy, Elsevier, vol. 30(15), pages 2221-2233.
    20. Makade, Rahul G. & Jamil, Basharat, 2018. "Statistical analysis of sunshine based global solar radiation (GSR) models for tropical wet and dry climatic Region in Nagpur, India: A case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 87(C), pages 22-43.
    21. Varun & Prakash, Ravi & Bhat, Inder Krishnan, 2009. "Energy, economics and environmental impacts of renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(9), pages 2716-2721, December.
    22. Katiyar, A.K. & Pandey, Chanchal Kumar, 2010. "Simple correlation for estimating the global solar radiation on horizontal surfaces in India," Energy, Elsevier, vol. 35(12), pages 5043-5048.
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