Abstract
Solar energy has greater advantages as it is both renewable and sustainable sources. Artificial Intelligence (AI) is an emerging trend, which makes machines learn and adhere like humans. ANN is the excellent and one of the main tools used in machine learning. The neural part in artificial neural network suggests brain-inspired systems, similar to the way we humans learn. ANN is basically used for finding patterns which are very complex for the programmer to extract and teach the machines to recognize. ANN-based models have been successfully upskilled to have different solar radiation variables, so as to improve the existing empirical and statistical approaches that are being used in solar radiation estimation. ANN has various applications in almost all areas like in aerospace, automotive, defense, mathematics, engineering, medicine, economics, meteorology, psychology, neurology, etc. Along with so many applications, ANN can also be used for the prediction of solar radiation. Radiations are the rays received by earth, analyzing the amount of radiation received will be helpful in efficient utilization of solar energy.
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References
Deepa, Y.R., Deependra, P.: Analytical study of ANN modeling based estimation of solar radiation. In: IOSR Journal of Engineering (IOSR JEN). National seminar cum workshop on “Data Science and Information Security 2019” (2019)
Tsoutsos, T., Frantzeskaki, N., Gekas, V.: Environmental impacts from the solar energy technologies. Energy Policy 33(3), 289–296 (2005). https://doi.org/10.1016/s0301-4215(03)00241-6
Ampratwum, D.B., Dorvlo, A.S.S.: Estimation of solar radiation from the number of sunshine hours. Appl. Energy 63(3), 161–167 (1999). https://doi.org/10.1016/s0306-2619(99)00025-2
Kumar, N., Sharma, S.P., Sinha, U.K., Nayak, Y.: Prediction of solar energy based on intelligent ANN modeling. Int. J. Renew. Energy Res. (S. P. Sharma et al.) 6(1) (2016)
Yadav, A.K., Chandel, S.S.: Solar radiation prediction using ANN techniques: a review. Renew. Sustain. Energy Rev. 5(4) (2001). Elsevier science Ltd.
Basheer, I., Hajmeer, M.: Artificial neural networks: fundamentals, computing, design, and application. J. Microbiol. Methods 43(1), 3–31 (2000). https://doi.org/10.1016/s0167-7012(00)00201-3
Zhang, J., Zhao, L., Deng, S., Xu, W., Zhang, Y.: A critical review of the models used to estimate solar radiation. Renew. Sustain. Energy Rev. 70, 314–329 (2017). https://doi.org/10.1016/j.rser.2016.11.124
Kumari Namrata, S.P., Sharma, S.B.L.S.: Comparison of different models for estimation of Global solar radiation in Jharkhand region. Smart Grid Renew. Energy 4, 348–352 (2013)
Ibeh G.F., Agbo G.A., Agbo, P.E., Ali, P.A.: Application of artificial neural networks for global solar radiation forecasting with temperature. Pelagia Res. Lib. Adv. Appl. Sci. Res. 3(1), 130–134 (2012)
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Yadav, D.R., Pandey, D., Choudhary, A., Mohanty, M.N. (2021). Analysis and Study of Solar Radiation Using Artificial Neural Networks. In: Mishra, D., Buyya, R., Mohapatra, P., Patnaik, S. (eds) Intelligent and Cloud Computing. Smart Innovation, Systems and Technologies, vol 194. Springer, Singapore. https://doi.org/10.1007/978-981-15-5971-6_8
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DOI: https://doi.org/10.1007/978-981-15-5971-6_8
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