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Fuzzy logic based modeling and estimation of global solar energy using meteorological parameters

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  • Rizwan, M.
  • Jamil, Majid
  • Kirmani, Sheeraz
  • Kothari, D.P.

Abstract

Global solar energy data is considered as the most important parameter in smart grid applications, particularly for sizing the photovoltaic system and demand driven supply. However the data of global solar energy is rarely available on hourly basis, even for those stations where measurement has already been done. Due to lack of such measured data, the estimation of global solar energy at the earth's surface is an important study in the present scenario to meet the energy requirement from green energy sources.

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  • 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.
    • Handle: RePEc:eee:energy:v:70:y:2014:i:c:p:685-691
    DOI: 10.1016/j.energy.2014.04.057
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    1. Kaplanis, S. & Kaplani, E., 2010. "Stochastic prediction of hourly global solar radiation for Patra, Greece," Applied Energy, Elsevier, vol. 87(12), pages 3748-3758, December.
    2. Kaplanis, S.N., 2006. "New methodologies to estimate the hourly global solar radiation; Comparisons with existing models," Renewable Energy, Elsevier, vol. 31(6), pages 781-790.
    3. Kaplanis, S. & Kaplani, E., 2007. "A model to predict expected mean and stochastic hourly global solar radiation I(h;nj) values," Renewable Energy, Elsevier, vol. 32(8), pages 1414-1425.
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    2. Fan, Junliang & Wu, Lifeng & Zhang, Fucang & Cai, Huanjie & Zeng, Wenzhi & Wang, Xiukang & Zou, Haiyang, 2019. "Empirical and machine learning models for predicting daily global solar radiation from sunshine duration: A review and case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 186-212.
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    4. 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.
    5. Mohamed Chaibi & EL Mahjoub Benghoulam & Lhoussaine Tarik & Mohamed Berrada & Abdellah El Hmaidi, 2021. "An Interpretable Machine Learning Model for Daily Global Solar Radiation Prediction," Energies, MDPI, vol. 14(21), pages 1-19, November.
    6. Ayodele, T.R. & Ogunjuyigbe, A.S.O., 2015. "Prediction of monthly average global solar radiation based on statistical distribution of clearness index," Energy, Elsevier, vol. 90(P2), pages 1733-1742.
    7. M. Sridharan, 2023. "Application of Generalized Regression Neural Network in Predicting the Performance of Solar Photovoltaic Thermal Water Collector," Annals of Data Science, Springer, vol. 10(1), pages 1-23, February.
    8. De Giorgi, M.G. & Malvoni, M. & Congedo, P.M., 2016. "Comparison of strategies for multi-step ahead photovoltaic power forecasting models based on hybrid group method of data handling networks and least square support vector machine," Energy, Elsevier, vol. 107(C), pages 360-373.
    9. Connolly, D., 2017. "Heat Roadmap Europe: Quantitative comparison between the electricity, heating, and cooling sectors for different European countries," Energy, Elsevier, vol. 139(C), pages 580-593.
    10. Torres-Ramírez, M. & Elizondo, D. & García-Domingo, B. & Nofuentes, G. & Talavera, D.L., 2015. "Modelling the spectral irradiance distribution in sunny inland locations using an ANN-based methodology," Energy, Elsevier, vol. 86(C), pages 323-334.
    11. 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.
    12. 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.
    13. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
    14. Jose Manuel Barrera & Alejandro Reina & Alejandro Maté & Juan Carlos Trujillo, 2020. "Solar Energy Prediction Model Based on Artificial Neural Networks and Open Data," Sustainability, MDPI, vol. 12(17), pages 1-20, August.
    15. Aggarwal, S.K. & Saini, L.M., 2014. "Solar energy prediction using linear and non-linear regularization models: A study on AMS (American Meteorological Society) 2013–14 Solar Energy Prediction Contest," Energy, Elsevier, vol. 78(C), pages 247-256.

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