Comparative study of the prediction of electrical energy from a photovoltaic system using the intelligent systems ANFIS and ANFIS-GA

Keywords: Photovoltaic systems, Solar power generation, Statistical methods, Genetic algorithms, ANFIS


Electrical energy generation with hydrocarbons accounts for about 38% of global CO2 emissions. Solar photovoltaic technology is a reliable alternative to reduce these emissions. To predict the electrical energy generation behavior in a photovoltaic system, we developed an adaptive neuro-fuzzy inference system (ANFIS) model which integrates an optimization through a genetic algorithm (GA). The evolutionary ANFIS-GA uses a geographical area's solar radiation and ambient temperature. This model uses the capacity for classification and identification of data patterns of neural networks, and through fuzzy modeling, it calculates the optimal membership functions and fuzzy rules. The ANFIS-GA model is developed using MATLAB® software and is trained with the acquired data weather station and the electrical power output of the photovoltaic system located in Hermosillo, Sonora, México. The above was compared under the same parameters with an ANFIS model based on a hybrid algorithm. Reach values of RSME of 259.41, MAE of 132.7, MAPE of 4.56 for the ANFIS-GA model; RSME of 295.26, MAE of 149.58, and MAPE of 6.98 for the ANFIS model, respectively. The results indicate that the ANFIS-GA model emulates the power output with better precision, thus providing a valuable planning tool to predict photovoltaic system behavior.


Arora, A. Arabameri, M. Pandey, et al., optimization of state-of-the-art fuzzy-metaheuristic ANFIS based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India, Science of the Total Environment (2020),

Alamoudi, R., Taylan, O., Aktacir, M. A., & Herrera-Viedma, E. (2021). Designing a Solar Photovoltaic System for Generating Renewable Energy of a Hospital: Performance Analysis and Adjustment Based on RSM and ANFIS Approaches. Mathematics, 9(22), 2929.

Aldair, Ammar A. & Obed, Adel A. & Halihal, Ali F., (2018). Design and implementation of ANFIS-reference model controller based MPPT using FPGA for photovoltaic system, Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 22022217.

Armaghani, D. J., & Asteris, P. G. (2021). A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength. Neural Computing and Applications, 33(9), 45014532.

Asuero, A. G., Sayago, A., & Gonzalez, A. G. (2006). The correlation coefficient: An overview. Critical reviews in analytical chemistry, 36(1), 4159.

Azimi, H., Bonakdari, H., Ebtehaj, I., Shabanlou, S., Ashraf Talesh, S. H., & Jamali, A. (2019). A pareto design of evolutionary hybrid optimization of ANFIS model in prediction abutment scour depth. Sādhanā, 44(7), 114.

Belkin, M., Hsu, D. J., & Mitra, P. (2018). Overfitting or perfect fitting? risk bounds for classification and regression rules that interpolate. Advances in neural information processing systems, 31. ISBN: 9781510884472.

Belkin, M., Hsu, D., Ma, S., & Mandal, S. (2019). Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences, 116(32), 1584915854.

Bendary, A. F., Abdelaziz, A. Y., Ismail, M. M., Mahmoud, K., Lehtonen, M., & Darwish, M. M. (2021). Proposed ANFIS Based Approach for Fault Tracking, Detection, Clearing and Rearrangement for Photovoltaic System. Sensors, 21(7), 2269.

Benesty, J., Chen, J., Huang, Y., & Cohen, I. (2009). Pearson correlation coefficient Noise reduction in speech processing (pp. 14): Springer, Berlin, Heidelberg.

Bilgili, M., Yildirim, A., Ozbek, A., Celebi, K., & Ekinci, F. (2021). Long short-term memory (LSTM) neural network and adaptive neuro-fuzzy inference system (ANFIS) approach in modeling renewable electricity generation forecasting. International Journal of Green Energy, 18(6), 578594.

Brio, M., & Molina, A. (2001). Redes neuronales y sistemas difusos. México, DF: Alfaomega: Ra-Ma. ISBN 8478977430, 9788478977437.

Brusamarello, C. Z., Di Domenico, M., Da Silva, C., & de Castilhos, F. (2020). A comparative study between multivariate calibration and artificial neural network in quantification of soybean biodiesel. Revista Mexicana de Ingeniería Química, 19(1), 123-132. DOI:

Carrascal, A., Díez Oliván, A., Font Fernández, J. M., & Manrique Gamo, D. (2009). Evolutionary generation of fuzzy knowledge bases for diagnosing monitored railway systems. ISBN 978-84-932064-6-8. pp. 191-198.

Cerecedo, L. O. L., Pitalúa-Díaz, N., Palafox, J. F. H., Marín, J. A., & Zavala, S. R. (2021, November). Intelligent predictive model of electrical power in photovoltaic systems through solar radiation and temperature on site. In 2021 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC) (Vol. 5, pp. 16). IEEE.

Chalapathy, R., Khoa, N. L. D., & Sethuvenkatraman, S. (2021). Comparing multi-step ahead building cooling load prediction using shallow machine learning and deep learning models. Sustainable Energy, Grids and Networks, 28, 100543.

Cui, Y., Wu, D., & Xu, Y. (2021, July). Curse of dimensionality for tsk fuzzy neural networks: Explanation and solutions. International Joint Conference on Neural Networks (IJCNN) (pp. 18). IEEE.

Di Piazza, A., Di Piazza, M. C., La Tona, G., & Luna, M. (2021). An artificial neural network-based forecasting model of energy-related time series for electrical grid management. Mathematics and Computers in Simulation, 184, 294305.

dos Santos, C. M., Escobedo, J. F., de Souza, A., da Silva, M. B. P., & Aristone, F. (2021). Prediction of solar direct beam transmittance derived from global irradiation and sunshine duration using anfis. International Journal of Hydrogen Energy, 46(55), 27905-27921.

Emami, M. R., Turksen, I. B., & Goldenberg, A. A. (1998). Development of a systematic methodology of fuzzy logic modeling. IEEE Transactions on Fuzzy Systems, 6(3), 346361.

Fernandez, A., Herrera, F., Cordon, O., del Jesus, M. J., & Marcelloni, F. (2019). Evolutionary fuzzy systems for explainable artificial intelligence: Why, when, what for, and where to?. IEEE Computational intelligence magazine, 14(1), 6981.

Ferrero Bermejo, J., Gomez Fernandez, J. F., Olivencia Polo, F., & Crespo Marquez, A. (2019). A review of the use of artificial neural network models for energy and reliability prediction. A study of the solar PV, hydraulic and wind energy sources. Applied Sciences, 9(9), 1844.

Figueroa-García, E., Farias-Cervantes, V. S., Segura-Castruita, M., Andrade-Gonzalez, I., Montero-Cortés, M. I., & Chávez-Rodríguez, A. M. (2021). Using artificial neural networks in prediction of the drying process of foods that are rich in sugars. Revista Mexicana de Ingeniería Química, 20(1), 161171.

García-Muñoz, F., Díaz-González, F., & Corchero, C. (2021). A novel algorithm based on the combination of AC-OPF and GA for the optimal sizing and location of DERs into distribution networks. Sustainable Energy, Grids and Networks, 27, 100497.

Gómez Vargas, E., Obregón Neira, N., & Socarras Quintero, V. (2010). Application of neuro-fuzzy anfis model vs neural network, to the predictive monthly mean flow problem in the bogotá river in villapinzón. Tecnura, 14(27), 1829. ISSN 0123-921X.

Gholamy, A., Kreinovich, V., & Kosheleva, O. (2018). Why 70/30 or 80/20 relation between training and testing sets: a pedagogical explanation.

Griffiths, D. (1980). A pragmatic approach to Spearman's rank correlation coefficient. Teaching Statistics, 2(1), 1013.

Hawkins, D. M. (2004). The problem of overfitting. Journal of chemical information and computer sciences, 44(1), 112.

Haznedar, B., & Kalinli, A. (2016). Training ANFIS using genetic algorithm for dynamic systems identification. International Journal of Intelligent Systems and Applications in Engineering, 4(1), 4447.

Hecht-Nielsen, R. (1992). Theory of the backpropagation neural network. In Neural networks for perception (pp. 6593). Academic Press.

Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665685.

Jang, J. S. R., Sun, C. T., & Mizutani, E. (1997). Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [Book Review]. IEEE Transactions on automatic control, 42(10), 14821484.

Jang, J. S. R., Sun, C. T., & Mizutani, E. (1997). Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [Book Review]. IEEE Transactions on automatic control, 42(10), 14821484. ISBN-13: 978-0132610667.

Jang, J. S., & Sun, C. T. (1995). Neuro-fuzzy modeling and control. Proceedings of the IEEE, 83(3), 378406.

Kar, S., Majumdar, S., Constales, D., Pal, T., & Dutta, A. (2020). A comparative study of multi objective optimization algorithms for a cellular automata model. Revista Mexicana de Ingeniería Química, 19(1), 299-311. DOI:

Khosravi, A., Malekan, M., Pabon, J. J. G., Zhao, X., & Assad, M. E. H. (2020). Design parameter modelling of solar power tower system using adaptive neuro-fuzzy inference system optimized with a combination of genetic algorithm and teaching learning-based optimization algorithm. Journal of Cleaner Production, 244, 118904.

Kohonen, T. (1988). An introduction to neural computing. Neural networks, 1(1), 316.

Koza, J. R. (1990, November). Genetically breeding populations of computer programs to solve problems in artificial intelligence. Proceedings of the 2nd International IEEE Conference on Tools for Artificial Intelligence (pp. 819827). IEEE.

Kröse, Ben & Krose, B. & van der Smagt, Patrick & Smagt, Patrick. (1993). An introduction to neural networks. J Comput Sci. 48.

Kukolj, D., & Levi, E. (2004). Identification of complex systems based on neural and Takagi-Sugeno fuzzy model. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 34(1), 272282.

Lippmann, R. (1987). An introduction to computing with neural nets. IEEE Assp magazine, 4(2), 422.

Ma, T., Yang, H., & Lu, L. (2014). Solar photovoltaic system modeling and performance prediction. Renewable and Sustainable Energy Reviews, 36, 304-315.

Matsumoto, Y., Norberto, C., Urbano, J. A., Dorantes, R., González, H., Pitalúa-Díaz, N., & Peña, R. (2019). 36 month performance of 60 kWp photovoltaic system in Mexico city. Revista Mexicana de Ingeniería Química, 18(3), 10171025.

Mellit, A., & Kalogirou, S. A. (2008). Artificial intelligence techniques for photovoltaic applications: A review. Progress in energy and combustion science, 34(5), 574632.

Nguyen, Q. H., Ly, H. B., Ho, L. S., Al-Ansari, N., Le, H. V., Tran, V. Q., ... & Pham, B. T. (2021). Influence of data splitting on performance of machine learning models in prediction of shear strength of soil. Mathematical Problems in Engineering, 2021.

Orove, J. O., Osegi, N. E., & Eke, B. O. (2015). A multi-gene genetic programming application for predicting students failure at school. arXiv preprint arXiv:1503.03211.

Penghui, L., Ewees, A. A., Beyaztas, B. H., Qi, C., Salih, S. Q., Al-Ansari, N., & Singh, V. P. (2020). Metaheuristic optimization algorithms hybridized with artificial intelligence model for soil temperature prediction: Novel model. IEEE Access, 8, 5188451904.

Pitalúa-Díaz, N., Arellano-Valmaña, F., Ruz-Hernandez, J. A., Matsumoto, Y., Alazki, H., Herrera-López, E. J., Hinojosa-Palafox, J. F., et al. (2019). An ANFIS-Based Modeling Comparison Study for Photovoltaic Power at Different Geographical Places in Mexico. Energies, 12(14), 2662. MDPI AG. Retrieved from

Rico-Contreras, J. O., Aguilar-Lasserre, A. A., Méndez-Contreras, J. M., Cid-Chama, G., & Alor-Hernández, G. (2014). Moisture content prediction in poultry litter to estimate bioenergy production using an artificial neural network. Revista Mexicana de Ingeniería Química, 13(3), 933955.

Ruz-Hernandez, J. A., Matsumoto, Y., Arellano-Valmaña, F., Pitalúa-Díaz, N., Cabanillas-López, R. E., Abril-García, J. H., & Velázquez-Contreras, E. F. (2019). Meteorological variables' influence on electric power generation for photovoltaic systems located at different geographical zones in Mexico. Applied Sciences, 9(8), 1649.

Seifi, A., Ehteram, M., Singh, V. P., & Mosavi, A. (2020). Modeling and uncertainty analysis of groundwater level using six evolutionary optimization algorithms hybridized with ANFIS, SVM, and ANN. Sustainability, 12(10), 4023.

Sexton, R. S., Hignite, M. A., Margavio, T., & Satzinger, J. (2001). Neural networks refined: using a genetic algorithm to identify predictors of IS student success. Journal of Computer Information Systems, 41(3), 4247. ISSN: 2380-2057.

Stojčić, M., Stjepanović, A., & Stjepanović, Đ. (2019). ANFIS model for the prediction of generated electricity of photovoltaic modules. Decision Making: Applications in Management and Engineering, 2(1), 3548.

Sugeno, M., & Kang, G. T. (1988). Structure identification of fuzzy model. Fuzzy sets and systems, 28(1), 1533.

Talebjedi, B., Khosravi, A., Laukkanen, T., Holmberg, H., Vakkilainen, E., & Syri, S. (2020). Energy modeling of a refiner in thermo-mechanical pulping process using ANFIS method. Energies, 13(19), 5113.

Tao, H., Ewees, A. A., Al-Sulttani, A. O., Beyaztas, U., Hameed, M. M., Salih, S. Q., & Yaseen, Z. M. (2021). Global solar radiation prediction over North Dakota using air temperature: development of novel hybrid intelligence model. Energy Reports, 7, 136157.

Tien Bui, D., Khosravi, K., Li, S., Shahabi, H., Panahi, M., Singh, V. P., ... & Bin Ahmad, B. (2018). New hybrids of anfis with several optimization algorithms for flood susceptibility modeling. Water, 10(9), 1210.

Vafaei, S., Rezvani, A., Gandomkar, M., & Izadbakhsh, M. (2015). Enhancement of grid-connected photovoltaic system using ANFIS-GA under different circumstances. Frontiers in Energy, 9(3), 322334.

Virgen-Navarro, L., Herrera-López, E. J., Corona-González, R. I., Arriola-Guevara, E., & Guatemala-Morales, G. M. (2016). Neuro-fuzzy model based on digital images for the monitoring of coffee bean color during roasting in a spouted bed. Expert Systems with Applications, 54, 162-169.

Wang, M., Zhang, Q., Chen, H., Heidari, A. A., Mafarja, M., & Turabieh, H. (2021). Evaluation of constraint in photovoltaic cells using ensemble multi-strategy shuffled frog leading algorithms. Energy Conversion and Management, 244, 114484.

Xia, J., Chen, H., Li, Q., Zhou, M., Chen, L., Cai, Z., ... & Zhou, H. (2017). Ultrasound-based differentiation of malignant and benign thyroid Nodules: An extreme learning machine approach. Computer methods and programs in biomedicine, 147, 3749.

Yazdanbaksh, O., Krahn, A., & Dick, S. (2013, June). Predicting solar power output using complex fuzzy logic. In 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS) (pp. 1243-1248). IEEE.

Ying, L. C., & Pan, M. C. (2008). Using adaptive network based fuzzy inference system to forecast regional electricity loads. Energy Conversion and Management, 49(2), 205211.

Yousif, J. H., Kazem, H. A., Alattar, N. N., & Elhassan, I. I. (2019). A comparison study based on artificial neural network for assessing PV/T solar energy production. Case Studies in Thermal Engineering, 13, 100407.

Zor, K., Timur, O., & Teke, A. (2017, June). A state-of-the-art review of artificial intelligence techniques for short-term electric load forecasting. In 2017 6th international youth conference on energy (IYCE) (pp. 1-7). IEEE.

How to Cite
Lara-Cerecedo, L., Pitalúa-Díaz, N., & Hinojosa-Palafox, J. (2023). Comparative study of the prediction of electrical energy from a photovoltaic system using the intelligent systems ANFIS and ANFIS-GA. Revista Mexicana De Ingeniería Química, 22(1), Ener2956.
Energy Engineering

Most read articles by the same author(s)