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Generalized Regression Neural Network Model Based Estimation of Global Solar Energy Using Meteorological Parameters

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  • M. Sridharan

    (K. Ramakrishnan College of Engineering)

Abstract

The global solar irradiance data plays a vital role in evaluating the performance of all the solar energy conversion devices. In general there are two methods to predict the performance of such irradiance, namely physical models and the machine learning models. This paper presents a generalized regression neural network model (a machine learning technique) for estimating the global solar irradiance using seasonal and meteorological factors as input parameters. Results obtained from this proposed generalized regression neural network approach are compared with the results estimated by extensively used machine learning based methodologies such as fuzzy and artificial neural network models. Such a comparative results clearly indicate that prediction accuracy of proposed generalized regression neural network model is in good agreement with experimentally measured values. The mean percentage error for using GRNN, fuzzy logic and artificial neural network are 3.55%, 4.64%, and 5.49%.

Suggested Citation

  • M. Sridharan, 2023. "Generalized Regression Neural Network Model Based Estimation of Global Solar Energy Using Meteorological Parameters," Annals of Data Science, Springer, vol. 10(4), pages 1107-1125, August.
    • Handle: RePEc:spr:aodasc:v:10:y:2023:i:4:d:10.1007_s40745-020-00319-4
    DOI: 10.1007/s40745-020-00319-4
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    References listed on IDEAS

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    1. Rizwan, M. & Jamil, Majid & Kirmani, Sheeraz & Kothari, D.P., 2014. "Fuzzy logic based modeling and estimation of global solar energy using meteorological parameters," Energy, Elsevier, vol. 70(C), pages 685-691.
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    4. Chen, S.X. & Gooi, H.B. & Wang, M.Q., 2013. "Solar radiation forecast based on fuzzy logic and neural networks," Renewable Energy, Elsevier, vol. 60(C), pages 195-201.
    5. Zeng, Jianwu & Qiao, Wei, 2013. "Short-term solar power prediction using a support vector machine," Renewable Energy, Elsevier, vol. 52(C), pages 118-127.
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