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03-01-2021

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

Author: M. Sridharan

Published in: Annals of Data Science | Issue 4/2023

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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%.

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Tien JM (2017) Internet of things, real-time decision making, and artificial intelligence. Ann Data Sci 4(2):149–178. https://doi.org/10.1007/s40745-017-0112-5CrossRef

Olson DL, Shi Y (2007) Introduction to business data mining. McGraw-Hill/Irwin, New York

Shi Y, Tian YJ, Kou G, Peng Y, Li JP (2011) Optimization based data mining: theory and applications. Springer, BerlinCrossRef

Sivakumar P, Christraj W, Sridharan M, Jayamalathi N (2012) Performance comparison of differently configured solar water heaters. Eur J Sci Res 91(1):23–31

Marimuthu M, Geetha P, Deepiha P, Sridharan M (2015) MATLAB simulation of transparent glass PV/T hybrid water collectors. In: IEEE 9th international conference on intelligent systems and control (ISCO), Coimbatore, pp 1–7. https://doi.org/10.1109/ISCO.2015.7282327

Voyant C, Notton G, Kalogirou S, Nivet M-L, Paoli C, Motte F, Fouilloy A (2017) Machine learning methods for solar radiation forecasting: a review. Renew Energy 105(2017):569–582. https://doi.org/10.1016/j.renene.2016.12.095CrossRef

Wang L, Kisi O, Zounemat-Kermani M, Salazar GA, Zhu Z, Gong W (2016) Solar radiation prediction using different techniques: model evaluation and comparison. Renew Sustain Energy Rev 2016(61):384–97. https://doi.org/10.1016/j.rser.2016.04.024CrossRef

Maier HR, Dandy GC (2000) Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Modell Softw 15(1):101–124. https://doi.org/10.1016/S1364-8152(99)00007-9CrossRef

Zeng J, Qiao W (2013) Short-term solar power prediction using a support vector machine. Renew Energy 52(1):118–127. https://doi.org/10.1016/j.renene.2012.10.009CrossRef

10.

Orfila A, Ballester JL, Oliver R, Alvarez A, Tintoré J (2002) Forecasting the solar cycle with genetic algorithms. Astron Astrophys 386(1):313–318. https://doi.org/10.1051/0004-6361:20020246CrossRef

11.

Chen SX, Gooi HB, Wang MQ (2013) Solar radiation forecast based on fuzzy logic and neural networks. Renew Energy 60:195–201. https://doi.org/10.1016/j.renene.2013.05.011CrossRef

12.

Bendu H, Deepak BBVL, Murugan S (2016) Application of GRNN for the prediction of performance 585 and exhaust emissions in HCCI engine using ethanol. Energy Convers Manag 122:165–173. https://doi.org/10.1016/j.enconman.2016.05.061CrossRef

13.

Rizwan M, Jamil M, Kirmani S, Kothari DP (2014) Fuzzy logic based modeling and estimation of 573 global solar energy using meteorological parameters. Energy 70(6):685–691. https://doi.org/10.1016/j.energy.2014.04.057CrossRef

14.

Solar radiation handbook (2008) SEC & IMD, Pune. http://www.indiaenvironmentportal.org.in/files/srd-sec.pdf

15.

Ghritlahre HK, Chandrakar P, Ahmad AA (2019) Comprehensive review on performance prediction of solar air heaters using artificial neural network. Ann Data Sci. https://doi.org/10.1007/s40745-019-00236-1CrossRef

16.

Ghritlahre HK, Prasad RK (2018) Development of optimal ANN model to estimate the thermal performance of roughened solar air heater using two different learning algorithms. Ann Data Sci 5:453–467. https://doi.org/10.1007/s40745-018-0146-3CrossRef

17.

Sridharan M (2020) Application of generalized regression neural network in predicting the performance of solar photovoltaic thermal water collector. Ann Data Sci. https://doi.org/10.1007/s40745-020-00273-1CrossRef

18.

Sridharan M (2020) Applications of artificial intelligence techniques in heat exchanger systems, advanced analytic and control techniques for thermal systems with heat exchangers. Academic Press, New York, pp 325–334. https://doi.org/10.1016/B978-0-12-819422-5.00015-3. ISBN: 978-0-12-819422-5CrossRef

19.

Sridharan M, Jayaprakash G, Chandrasekar M, Vigneshwar P, Paramaguru S, Amarnath K (2018) Prediction of solar photovoltaic/thermal collector power output using fuzzy logic. J Sol Energy Eng 140(6):1–6. https://doi.org/10.1115/1.4040757CrossRef

20.

Sridharan M (2020) Application of generalized regression neural network in predicting the performance of natural convection solar dryer. J Sol Energy Eng 142(3):1–7. https://doi.org/10.1115/1.4047824CrossRef

21.

Sridharan M (2020) Predicting performance of double pipe parallel and counter flow heat exchanger using fuzzy logic. J Therm Sci Eng Appl 12(3):1–11. https://doi.org/10.1115/1.4044696CrossRef

22.

Sridharan M, Jayaprakash G (2019) Verification and validation of solar photovoltaic thermal water collectors performance using fuzzy logic. J Verif Valid Uncert 4(4):1–8. https://doi.org/10.1115/1.4045895CrossRef

23.

Sridharan M (2020) Application of fuzzy logic expert system in predicting cold and hot fluid outlet temperature of counter-flow double-pipe heat exchanger advanced analytic and control techniques for thermal systems with heat exchangers. Academic Press, New York, pp 307–323. https://doi.org/10.1016/B978-0-12-819422-5.00014-1. ISBN: 978-0-12-819422-5CrossRef

24.

Sridharan M, Shri Balaji S (2020) Application of generalized regression neural network in predicting the thermal performance of solar flat plate collector systems. J Therm Sci Eng Appl 13(2):1–11. https://doi.org/10.1115/1.4047824CrossRef

25.

Sridharan M, Devi R, Dharshini CS, Bhavadarani M (2019) IoT Based performance monitoring and control in counter flow double pipe heat exchanger. Internet Things 5:34–40. https://doi.org/10.1016/j.iot.2018.11.002CrossRef

Title: Generalized Regression Neural Network Model Based Estimation of Global Solar Energy Using Meteorological Parameters
Author: M. Sridharan
Publication date: 03-01-2021
Publisher: Springer Berlin Heidelberg
Published in: Annals of Data Science / Issue 4/2023
Print ISSN: 2198-5804
Electronic ISSN: 2198-5812
DOI: https://doi.org/10.1007/s40745-020-00319-4

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