ANFIS model for the prediction of generated electricity of photovoltaic modules

  • Mirko Stojčić University of East Sarajevo, Faculty of Transport and Traffic Engineering, Doboj, Bosnia and Herzegovina
  • Aleksandar Stjepanović University of East Sarajevo, Faculty of Transport and Traffic Engineering, Doboj, Bosnia and Herzegovina
  • Đorđe Stjepanović Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia
Keywords: prediction, ANFIS (Adaptive Neuro Fuzzy Inference System), photovoltaic modules, artificial neural networks, fuzzy logic, RMSE (Root Mean Square Error)

Abstract

The fact that conventional energy sources are exhaustive and limited are increasingly encouraging research in the field of alternative and renewable energy sources. The electricity generated by solar photovoltaic modules and panels occupies an ever greater percentage in total electricity production, so it is clear that photovoltaic systems are increasingly integrating with the existing electricity network into one system or functioning as autonomous systems. The aim of the research is to create a model based on the principles of the fuzzy logic and artificial neural networks that will perform the task of predicting the maximum energy of photovoltaic modules as accurately as possible. The prediction should facilitate work in planning production and consumption, system management, economic analysis. The most important methods used in the research are modeling and simulation. Input and output variables are selected and in the ANFIS (Adaptive Neuro Fuzzy Inference System) model a set of their values is presented. Based on them it comes to the function of dependency. The prediction rating of the created model was performed on a separate data set for testing and a model with the lowest average test error value was selected. The performance of the model was compared with the mathematical model through sensitivity analysis, which led to the conclusion that the ANFIS model gives more accurate results.

Downloads

Download data is not yet available.

References

Antonanzas, J., Osorio, N., Escobar, R., Urraca, R., Martinez-de-Pison, F. J. & Antonanzas-Torres, F. (2016). Review of photovoltaic power forecasting. Solar Energy, 136, 78-111.

Bašić, B. D., Čupić, M., & Šnajder, J. (2008). Umjetne neuronske mreže. Zagreb: Fakultet elektrotehnike i računarstva.

Castaner, L., & Silvestre S. (2002). Modelling Photovoltaic Systems using PSpice. England: John Wiley & Sons Ltd, The Atrium, Southern Gate ,Chichester West Sussex.

Chandani, S., & Anamika, J. (2014). Solar Panel Mathematical Modelig Using Simulink. International Journal of Engineering Research and Applications, 4 (5), 67-72

Ding, M., Wang, L., & Bi, R. (2011). An ANN-based approach for forecasting the power output of photovoltaic system. Procedia Environmental Sciences, 11, 1308-1315.

Guifang, G. (2014). Modeling of Solar photovoltaic cell and output characteristic simulation based on Simulink, Journal of Chemical and Pharmaceutical Research, 6 (7), 1791-1795.

Gules, R., Pacheco, J. D. P., Hey, H. L., & Imhoff, J. (2008). A maximum power point tracking system with parallel connection for PV stand-alone applications. IEEE Transactions on Industrial Electronics, 55(7), 2674-2683.

Mahmodian, M. S., Rahmani, R., Taslimi, E., & Mekhilef, S. (2012). Step by step analyzing, modeling and simulation of single and double array PV system in different environmental variability. In Proceedings of International Conference on Future Environment and Energy (pp. 37-42).

Mandal, P., Madhira, S. T. S., Meng, J., & Pineda, R. L. (2012). Forecasting power output of solar photovoltaic system using wavelet transform and artificial intelligence techniques. Procedia Computer Science, 12, 332-337.

Mellit, A., (2009). Recurrent neural network-based forecasting of the daily electricity generation of a Photovoltaic power system. In Ecological Vehicle and Renewable Energy (EVER), Monaco, March, 26-29.

Rasit, A. T. A. (2009). An adaptive neuro-fuzzy inference system approach for prediction of power factor in wind turbines. IU-Journal of Electrical & Electronics Engineering, 9(1), 905-912.

Saberian, A., Hizam, H., Radzi, M. A. M., Ab Kadir, M. Z. A., & Mirzaei, M. (2014). Modelling and prediction of photovoltaic power output using artificial neural networks. International Journal of Photoenergy, 2014, 1-10.

Salleh, M. N. M., & Hussain, K. (2016). A review of training methods of ANFIS for applications in business and economics. International Journal of u-and e-Service, Science and Technology, 9(7), 165-172.

Wang, J., Ran, R., & Zhou, Y. (2017). A Short-Term Photovoltaic Power Prediction Model Based on an FOS-ELM Algorithm. Applied Sciences, 7(4), 423.

Wolff, B., Lorenz, E., & Kramer, O. (2016). Statistical learning for short-term photovoltaic power predictions. In Computational sustainability (pp. 31-45). Springer, Cham.

Zhu, H., Li, X., Sun, Q., Nie, L., Yao, J., & Zhao, G. (2015). A power prediction method for photovoltaic power plant based on wavelet decomposition and artificial neural networks. Energies, 9(1), 1-15.

Zhu, H., Lian, W., Lu, L., Dai, S., & Hu, Y. (2017). An improved forecasting method for photovoltaic power based on adaptive BP neural network with a scrolling time window. Energies, 10(10), 1-18.
Published
2019-02-19
How to Cite
Stojčić, M., Stjepanović, A., & Stjepanović, Đorđe. (2019). ANFIS model for the prediction of generated electricity of photovoltaic modules. Decision Making: Applications in Management and Engineering, 2(1), 35-48. Retrieved from https://www.dmame.org/index.php/dmame/article/view/28