FORECASTING EVENTUAL OPERATIONAL PERFORMANCE OF THE SWH SYSTEM USING ANN APPROACH- SPECIAL REFERENCE TO BHOPAL (M.P.)
DOI:
https://doi.org/10.29121/shodhkosh.v5.i5.2024.4711Abstract [English]
Solar radiation is an important parameter in solar energy application, while this parameter is directly related to the performance of solar thermal and photovoltaic systems. The present research work mainly focused on the implementation of ANN technique to predict Operational Performance of the SWH System Using ANN Approach. A distinctive method is used to predict how well the solar-powered water heater in Bhopal City will function. This is done by combining GSR as well as SWH ANN models. The output of GSR from the GSR-ANN simulation of Bhopal city is used as a parameter for input in the combination ANN model for the solar-powered water heating system. The output that was obtained is compared to the values that were tested. The error study showed that the mixed ANN model that was created is the best option for predicting SWH performance, as it has the lowest RMSE and MAPE along with the highest R.
References
A. K. Yadav, H. Malik, and S.S. Chandel, “Selection of most relevant input parameters using WEKA for artificial neural network based solar radiation prediction models,” Renewable and Sustainable Energy Reviews, vol. 31, pp. 509-519, 2014. DOI: https://doi.org/10.1016/j.rser.2013.12.008
Adhikari, A, Dotel, UR, Pokhrel, R & Hagen, ST 2013, “Thermal efficiency test of locally manufactured solar flat plate collector of Nepal”, IEEE, pp. 1- 4. DOI: https://doi.org/10.1109/IYCE.2013.6604153
Aminmohammad Saberian, Hizam H, Radzi MAM,Ab Kadir MZA, and Maryam Mirzaei.,” Research Article Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Network”, International Journal of Photo energy, Article ID 469701,10 pages, 2014. DOI: https://doi.org/10.1155/2014/469701
Amit Kumar Yadav, Chandel SS., “Solar energy potential assessment of western Himalayan Indian state of Himachal Pradesh using J48 algorithm of WEKA in ANN based prediction model”, Renewable Energy, 75, pp.675-693, 2015. DOI: https://doi.org/10.1016/j.renene.2014.10.046
Arekete, SA 2013, “Performance of solar water heater in Akure, Nigeria”, Journal of Energy Technologies and Policy, vol. 03, no. 6, pp. 01-08. DOI: https://doi.org/10.9790/3021-03810103
Assi AH, Shamisi M & Hejase HA 2012, “MATLAB tool for predicting the global solar radiation in UAE”, International Conference on Renewable Energies for Developing Countries (REDEC), pp. 1-8. DOI: https://doi.org/10.1109/REDEC.2012.6416698
Ayompe, LM & Duffy, A 2013, “Analysis of the thermal performance of a solar water heating system with flat plate collectors in a temperate climate”, Applied Thermal Engineering, vol. 58, pp. 447- 454. DOI: https://doi.org/10.1016/j.applthermaleng.2013.04.062
B. Ihya, A. Mechaqrane, R. Tadili, and M. N. Bargach, “Prediction of hourly and daily diffuse solar fraction in the city of Fez (Morocco),” Theor Appl Climatol, vol. 120, issue 3, pp. 737-749. 2014. DOI: https://doi.org/10.1007/s00704-014-1207-y
Egeonu, DI, Njoku, HO, Okolo PN, Enibe SO., “Comparative assessment of temperature-based ANN and angstrom type models for predicting global solar radiation”, Advances in Intelligent Systems and Computing, 334, pp.109-122,2015. DOI: https://doi.org/10.1007/978-3-319-13572-4_9
Fariba Besharat, Dehghan, AA, & Faghih, AR 2013, “Empirical models for estimating global solar radiation: A review and case study”, Renewable and Sustainable Energy Reviews, vol. 21, pp. 798-821. DOI: https://doi.org/10.1016/j.rser.2012.12.043
Javier Almorox, Monica Bocco, & Enrique Willington 2013, “Estimation of daily global solar radiation from measured temperatures at Cañada de Luque, Córdoba, Argentina”, Renewable Energy, vol. 60, pp. 382-387. DOI: https://doi.org/10.1016/j.renene.2013.05.033
Jianwu Zeng and Wei Qiao., “Short-Term Solar Power Prediction using an RBF Neural Network”, IEEE Power and Energy Society General Meeting, 2011. DOI: https://doi.org/10.1109/PES.2011.6039204
Juhi Joshi., “A comparative study of ANN and angstrom Prescott model in the context of solar radiation analysis”, IPASJ- International Journal of Computer Science (IIJCS), 2(3), March 2014.
K. Chiteka, and C.C. Enweremadu, “Prediction of global horizontal solar irradiance in Zimbabwe using artificial neural networks,” Journal of Cleaner Production, vol. 135, 2016, pp. 701-711, 2016. DOI: https://doi.org/10.1016/j.jclepro.2016.06.128
Koca, A, Oztop, HF, Varol, Y & Koca, GO 2011, “Estimation of solar radiation using artificial neural networks with different input parameters for mediterranean region of Anatolia in turkey”, Expert Systems with Applications, vol. 38, no. 7, pp. 8756-8762. DOI: https://doi.org/10.1016/j.eswa.2011.01.085
M. Shaddel, D. S. Javan, and P. Baghernia, “Estimation of hourly global solar irradiation on tilted absorbers from horizontal one using artificial neural network for case study of Mashhad,” Renewable and Sustainable Energy Reviews, vol. 53, 59-67, 2016. DOI: https://doi.org/10.1016/j.rser.2015.08.023
M. Vakilia, S-R. Sabbagh-Yazdi, K. Kalhorb, and S. Khosrojerdi, “Using artificial neural networks for prediction of global solar radiation in Tehran considering particulate matter air pollution,” Energy Procedia, vol. 74, pp.1205-1212, 2015. DOI: https://doi.org/10.1016/j.egypro.2015.07.764
Mohammadi, K, Shamshirband, S, Tong, CW, Arif, M, Petković, D & Ch, S 2015, “A new hybrid support vector machine–wavelet transform approach for estimation of horizontal global solar radiation”, Energy Conversion and Management, vol. 92, pp. 162-171. DOI: https://doi.org/10.1016/j.enconman.2014.12.050
Naveen Sharma, MK 2012, “Applications of artificial neural network in solar thermal systems: A review “, Proceedings of the National Conference on Trends and Advances in Mechanical Engineering, pp. 188-194.
Ozgur Celik, Ahmet Teke, & Basak Yıldırım, H 2016, “The optimized artificial neural network model with Levenberg Marquardt algorithm for global solar radiation estimation in eastern mediterranean region of turkey”, Journal of Cleaner Production, vol. 116, pp. 1-12. DOI: https://doi.org/10.1016/j.jclepro.2015.12.082
Premalatha N, Dr. ValanArasu A., “Estimation of global solar radiation in India using artificial neural network”, International Journal of Engineering Science & Advanced Technology, 2(6), pp.1715 – 1721, 2012.
Qazi, A, Fayaz, H, Wadi, A, Raj, RG, Rahim, N & Khan, WA 2015, “The artificial neural network for solar radiation prediction and designing solar systems: a systematic literature review”, Journal of Cleaner Production, vol. 104, pp. 1-12. DOI: https://doi.org/10.1016/j.jclepro.2015.04.041
Rajesh Kumar, Aggarwal RK and Sharma J D.,” New Regression model to Estimate Global Solar Radiation Using Artificial Neural Network”, Advances in Energy Engineering (AEE) ,1(3), pp.66 -73, 2013.
Renno, C, Petito, F, Gatto, A 2015, “Artificial neural network models for predicting the solar radiation as input of a concentrating photovoltaic system”, Energy Conversion and Management, vol. 106, pp. 999-1012. DOI: https://doi.org/10.1016/j.enconman.2015.10.033
Shukla, R, Sumathy, K, Erickson, P & Gong, J 2013, “Recent advances in the solar water heating systems: A review”, Renewable and Sustainable Energy Reviews, vol. 19, pp. 173-190. DOI: https://doi.org/10.1016/j.rser.2012.10.048
Sivamadhavi V and R Samuel Selvaraj R.,” Prediction `of monthly mean daily global solar radiation using Artificial Neural Network”, J. Earth Syst. Sci. ,121(6), pp.1501–1510,2012. DOI: https://doi.org/10.1007/s12040-012-0235-1
Tamer Khatib, Azah Mohamed, Marwan Mahmoud and Sopian K., “An Assessment of Diffuse Solar Energy Models in Terms of Estimation Accuracy (Malaysia)”, Energy Procedia, 14, pp.2066- 2074, 2012. DOI: https://doi.org/10.1016/j.egypro.2011.12.1209
T. Khatib, A. Mohamed, K. Sopian, and M. Mahmoud, “Modeling of daily solar energy on a horizontal surface for five main sites in Malaysia,” International Journal of Green Energy, vol. 8, issue 8, pp.795-819, 2011. DOI: https://doi.org/10.1080/15435075.2011.602156
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