NONLINEAR CLUSTERED ADAPTIVE-NETWORK-BASED FUZZY INFERENCE SYSTEM MODEL FOR HOURLY SOLAR IRRADIATION ESTIMATION
DOI:
https://doi.org/10.59277/RRST-EE.2023.68.1.1Keywords:
Solar irradiation, ANFIS, grid partitioning, fuzzy C-means, subtractive clusteringAbstract
Solar energy occupies an important place among the various sources of renewable energy. A precise knowledge of the distribution of solar irradiation in a specified location is needed before any solar irradiation system installation. This paper introduces a nonlinear clustering, adaptive-network-based fuzzy inference system (ANFIS) model to estimate the hourly solar irradiation data using meteorological inputs and clustering algorithms: grid partitioning, subtractive clustering, and fuzzy c-means. Comparing these clustering algorithms is investigated to classify the inputs into clusters, which helps the solar irradiation estimation model build better. This method's advantage is understanding and simplifying the nonlinearity presented in the input’s datasets. Moreover, the FCM algorithm gives the best results from comparing the testing data; the RMSE is 43.2274 W/m2, and MSE equals 2001.34 W/m2 with an R2 equal to 0.9893.
References
(1) M.J. Pickl, The renewable energy strategies of oil majors – From oil to energy? Energy Strategy Reviews, 26, pp. 100370-100378 (2019).
(2) R. Dutta, K. Chanda, R. Maity, Future of solar energy potential in a changing climate across the world: A CMIP6 multi-model ensemble analysis, Renew Energy, 188, pp. 819–829 (2022).
(3) T. Güney, Solar energy and sustainable development: evidence from 35 countries, International Journal of Sustainable Development & World Ecology, 29, 2, pp. 187–194 (2021).
(4) F. Antonanzas-Torres, R. Urraca, J. Polo, O. Perpiñán-Lamigueiro, R. Escobar, Clear sky solar irradiance models: A review of seventy models, Renewable and Sustainable Energy Reviews, 107, pp. 374–387 (2019).
(5) Teke, H.B. Yildirim, Ö. Çelik, Evaluation and performance comparison of different models for the estimation of solar radiation, Renewable and Sustainable Energy Reviews, 50, pp. 1097–1107(2015).
(6) B.M. Lopes, A.M.G. Costa, W. Uturbey, Comparative analysis of estimation models for monthly average hourly solar radiation, Journal of Control, Automation and Electrical Systems, pp. 1–18 (2022).
(7) A.H. Ajil, H.A. AL-Rikabi Rashad, An empirical model for calculating monthly average daily global solar radiation based on temperature for five cities of different climates in Iraq, Journal of Applied Science and Engineering, 21, 4, pp. 497–506 (2018).
(8) A.S. Tomlin, R. Crook, C.J. Smith, J. Gooding, Solar resource estimation using a radiative transfer with shading (RTS) model, 31st European Photovoltaic Solar Energy Conference and Exhibition, 1, pp. 2808–2813 (2015).
(9) R. Tapimo, R. Tapimo, M. Lazard, G.L. Ymeli, G.L. Ymeli, D. Yemele, Radiative transfer model for ground surface irradiance estimation: clear sky, JOSA A, 38, 11, pp. 1640–1646 (2021).
(10) J. Zhang, L. Zhao, S. Deng, W. Xu, Y. Zhang, A critical review of the models used to estimate solar radiation, Renewable and Sustainable Energy Reviews, 70, pp. 314–329 (2017).
(11) H.B. Yıldırım, A. Teke, F. Antonanzas-Torres, Evaluation of classical parametric models for estimating solar radiation in the Eastern Mediterranean region of Turkey, Renewable and Sustainable Energy Reviews, 82, pp. 2053–2065 (2018).
(12) B.S. Masabi, Z. Aghashariatmadari, S. Hejabi, Evaluation the efficiency of a parametric model based on MODIS data for solar radiation estimation in comparison with some empirical models, Arabian Journal of Geosciences, 14, 15, pp. 1–13 (2021).
(13) Jahani, Y. Dinpashoh, A. Raisi Nafchi, Evaluation and development of empirical models for estimating daily solar radiation, Renewable and Sustainable Energy Reviews, 73, pp. 878–891(2017).
(14) M.A. Hassan, A. Khalil, S. Kaseb, M.A. Kassem, Independent models for estimation of daily global solar radiation: A review and a case study, Renewable and Sustainable Energy Reviews, 82, pp. 1565–1575 (2018).
(15) A. Angstrom, Solar, and terrestrial radiation. Report to the international commission for solar research on actinometric investigations of solar and atmospheric radiation, Quarterly Journal of the Royal Meteorological Society, 50, 210, pp. 121–126 (1924).
(16) Teke, H.B. Yildirim, Ö. Çelik, Evaluation and performance comparison of different models for the estimation of solar radiation, Renewable and Sustainable Energy Reviews, 50, pp. 1097–1107 (2015).
(17) D. Patel, S. Patel, P. Patel, M. Shah, Solar radiation and solar energy estimation using ANN and Fuzzy logic concept: A comprehensive and systematic study, Environmental Science and Pollution Research, 29, 22, pp. 32428–32442 (2022).
(18) H. Jiang, N. Lu, J. Qin, W. Tang, L. Yao, A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data, Renewable and Sustainable Energy Reviews, 114, p. 109327 (2019).
(19) K. Benmouiza, A. Cheknane, Quantification of solar radiation in Algeria, application to the sizing of photovoltaic systems, Abou Bekr Belkaid University, Tlemcen, 2015. Accessed: Jun. 22, 2022.
(20) O.D. Samuel, M.O. Okwu, O.J. Oyejide, E. Taghinezhad, A. Afzal, M. Kaveh, Optimizing biodiesel production from abundant waste oils through empirical method and grey wolf optimizer, Fuel, 281, p. 118701 (2020).
(21) B.B. Uzun, M. Kiliç, N. Özbay, A.E. Pütün, E. Pütün, Biodiesel production from waste frying oils: Optimization of reaction parameters and determination of fuel properties, Energy, 44, 1, pp. 347–351(2012).
(22) K. Benmouiza, A. Cheknane, Clustered ANFIS network using fuzzy c-means, subtractive clustering, and grid partitioning for hourly solar radiation forecasting, Theor. Appl. Climatol., 137, 1–2, pp. 31–43 (2019).
(23) Yu, Estimation of pile settlement socketed to rock applying hybrid ALO-ANFIS and GOA-ANFIS approaches, Journal of Applied Science and Engineering, 25, 6, pp. 979–992(2022).
(24) J.C. Bezdek, Pattern recognition with fuzzy objective function algorithms, Pattern Recognition with Fuzzy Objective Function Algorithms (1981).
(25) J.C. Dunn, A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters, Journal of Cybernetics, 3, 3, pp. 32–57 (2008).
(26) R.R. Yager, D.P. Filev, Generation of fuzzy rules by mountain clustering, Journal of Intelligent & Fuzzy Systems, 2, 3, pp. 209–219 (1994).
