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Prediction of the output power of photovoltaic module using artificial neural networks model with optimizing the neurons number

1Middle Technical University (MTU), Baqubah Technical Institute, Renewable Energy Department, Baghdad, 10074, Iraq

2Ministry of Higher Education and Scientific Research, Department of Research and Development, Baghdad, 10074, Iraq

3Ministry of Interior, Directorate of Arab and International Cooperation, Department of Educational Affairs, Baghdad, Iraq

Received: 31 Oct 2022; Revised: 27 Jan 2023; Accepted: 16 Feb 2023; Available online: 30 Mar 2023; Published: 15 May 2023.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2023 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Artificial neural networks (ANNs) is an adaptive system that has the ability to predict the relationship between the input and output parameters without defining the physical and operation conditions. In this study, some queries about using ANN methodology are simply clarified especially about the neurons number and their relationship with input and output parameters. In addition, two ANN models are developed using MATLAB code to predict the power production of a polycrystalline PV module in the real weather conditions of Iraq. The ANN models are then used to optimize the neurons number in the hidden layers. The capability of ANN models has been tested under the impact of several weather and operational parameters. In this regard, six variables are used as input parameters including ambient temperature, solar irradiance and wind speed (the weather conditions), and module temperature, short circuit current and open circuit voltage (the characteristics of PV module). According to the performance analysis of ANN models, the optimal neurons number is 15 neurons in single hidden layer with minimum Root Mean Squared Error (RMSE) of 2.76% and 10 neurons in double hidden layers with RMSE of 1.97%.  Accordingly, it can be concluded that the double hidden layers introduce a higher accuracy than the single hidden layer. Moreover, the ANN model has proven its accuracy in predicting the current and voltage of PV module. 

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Keywords: Photovoltaic; Power production; Artificial neural networks; Neurons

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  1. Abiodun, O.I., Aman J., Abiodun E. O., Kemi, V. D., Nachaat, A. M., Humaira, A. (2018). State-of-the-art in artificial neural network applications: A survey. Heliyon, 4(11), 1-41.
  2. Barron, A.R. (1994). Neural networks: A review from a statistical perspective. Statistical Science, 9 (1), 33-35.
  3. Cristian-Dragos, D., Adrian, G. & Calin, E. (2016). Solar Photovoltaic Energy Production Forecast Using Neural Network. Procedia Technology, 22, 808-815.
  4. Cybenko, G. (1989). Approximation by super positions of a sigmoidal function. Mathematical Control Signals Systems, 2, 303-314.
  5. Elsheikh, A.H., Sharshir SW, Elaziz, M.A., Kabeel, A.E., Guilan, W. & Haiou, Z. (2019). Modelling of solar energy systems using artificial neural network: a comprehensive review. Solar Energy, 180 (1), 622-639.
  6. Gideon, K., Francis, N., Christopher, M. & Robert K. (2021). Evaluation of thermal interface materials in mediating PV cell temperature mismatch in PV-TEG power generation. Energy Reports, 7, 1636-1650.
  7. Hagan, M.T. & Menhaj, M.B. (1995). Training feed forward networks with the Marquardt algorithm. IEEE Trans. Neural Network, 5(6), 989-993.
  8. Hecht-Nielsen, R. (1990) Neurocomputing. Addison-Wesley, Longman Publishing Co., Inc. 75 Arlington Street, Suite 300 Boston, MA United States.
  9. Hertz, J., Krogh, A. & Palmer, R.G. (2018). Introduction to The Theory of Neural Computation. CRC Press-Taylor & Francis Group, 6000 Broken Sound Parkway NW, Boca Raton, London New York.
  10. Ismail, K. & Gencoglu M.T. (2019). Predicting power production from a photovoltaic panel through artificial neural networks using atmospheric indicators. Neural Computing and Applications, 31 (8), 3573-3586.
  11. Kalogirou, S.A, Neocleous, C.C. & Schizas, C. N. (1998). Artificial neural networks for modelling the starting-up of a solar steam generator. Applied Energy, 60 (2), 89-100.
  12. Kalogirou, S.A. (1996). Artificial neural networks for predicting the local concentration ratio of parabolic trough collectors. Freiburg, Germany: Proceedings of the International Conference EuroSun’96, 470-475.
  13. Kalogirou, S.A., Neocleous, C.E, & Schizas, C.N. (1996). A comparative study of methods for estimating intercept factor of parabolic trough collectors. London, UK: Proceedings of the International Conference EANN’96,5-8.
  14. Kalogirou, S.A., Neocleous, C.E., Schizas, C.N. (1997). Artificial neural networks for the estimation of the performance of a parabolic trough collector steam generation system. Stockholm, Sweden: Proceedings of the International Conference EANN’97, 227-232.
  15. Kang, S. (1991). An Investigation of the Use of Feed forward Neural Networks for Forecasting. Ph.D. Thesis, Kent State University.
  16. Laarabi, B., May, T. O., Dahlioui, D., Bassam, A., Flota-Banuelos, M. & Barhdadi, A. (2019). Artificial neural network modeling and sensitivity analysis for soiling effects on photovoltaic panels in Morocco. Superlattices and Microstructures, 127, 139-150.
  17. Lachtermacher, G., Fuller, J.D. (1995). Back propagation in time-series forecasting. Journal of Forecasting, 14 (4), 381-393.
  18. Lippmann, R.P. (1987). An introduction to computing with neural nets. IEEE Magazine, 4(2), 4-22.
  19. Liu, L., Diran L., Qie S., Hailong, L. & Ronald, W. (2017). Forecasting Power Output of Photovoltaic System Using A BP Network Method. Energy Procedia, 142, 780-786.
  20. MacKay, D.J.C. (1992). A practical Bayesian framework for back propagation networks. Neural Computation, 4 (3), 448-72.
  21. Mellit, A., Saglam, S. & Kalogirou, S.A. (2013). Artificial neural network-based model for estimating the produced power of a photovoltaic module. Renewable Energy, 60, 71-78.
  22. Mittal, M. Birinchi B., Sahaj, S. & Anshu, M.G. (2018). Performance prediction of PV module using electrical equivalent model and artificial neural network. Solar Energy, 176, 104-117.
  23. Mohammad, A.T., Al-Obaidi, M.A., Hameed, E.M. Basheer, I.M. & Mujtaba I. M. (2020). Modelling the chlorophenol removal from wastewater via reverse osmosis process using a multilayer artificial neural network with genetic algorithm. Journal of Water Process Engineering, 33, 1-10.
  24. Mohammad, A.T., Sohif, B. M., Sulaiman, M.Y., Sopian, K., & Al-abidi A.A. (2013). Artificial neural network analysis of liquid desiccant dehumidifier performance in a solar hybrid air-conditioning system. Applied Thermal Engineering, 59 (1-2), 389-397.
  25. Mohanraj, M., Jayaraj, S. & Muraleedharan, C. (2009). Performance prediction of a direct expansion solar assisted heat pump using artificial neural networks. Appl Energy. 86(9):1442-1449.
  26. Mubiru, J. (2011). Using Artificial Neural Networks to Predict Direct Solar Irradiation. Advances in Artificial Neural Systems, 2011, 1687-7594.
  27. Nayak, S. C., Misra, B. B. & Behera, H.S. (2017). Artificial chemical reaction optimization of neural networks for efficient prediction of stock market indices. Ain Shams Engineering Journal, 8(3), 371-390.
  28. Pontes, F.J., de Paiva, A.P., Balestrassi, P.P., Ferreira, J.R. and da Silva, M.B. (2012). Optimization of radial basis function neural network employed for prediction of surface roughness in hard turning process using Taguchi’s orthogonal arrays. Expert Systems with Applications, 39 (9), 7776-7787.
  29. Priyanka, S. & Ravindra N.M. (2012). Temperature dependence of solar cell performance-an analysis. Solar Energy Materials and Solar Cells, 101, 36-45.
  30. Raj, A.K., Kunal, G., Srinivas, M. & Jayaraj, S. (2019). Performance analysis of a double-pass solar air heater system with asymmetric channel flow passages. Journal of Thermal Analysis and Calorimetry, 136 (1), 21-38.
  31. Rumelhart, D.E., Hinton, G.E. & Williams, R.J. (1986). Learning representations by backpropagating Errors. Nature, 323, 533-536;
  32. Sanjay, C., Jyothi, C. & Chin, C.W. (2006). A study of surface roughness in drilling using mathematical analysis and neural networks. International Journal Advance Manufacturing Technology, 29, 846-852.
  33. Shaft, I. A., Shah S.I. & Kashif, F.M. (2006). Impact of varying neurons and hidden layers in neural network architecture for a time frequency application. In Proceedings of the 10th IEEE International Multitopic Conference (INMIC), Islamabad, Pakistan, (23-24) 188-193.
  34. Siti, A. J., Flora, C., Mohd, H. A.W, Nur Hanis, M. R. & Muhammad, F. O. (2018). Prediction of Photovoltaic (PV) Output Using Artificial Neutral Network (ANN) Based on Ambient Factors. Journal of Physics: Conference Series 1049, (2018) 012088. https://doi.og/10.1088/1742-6596/1049/1/012088
  35. Srinivasan, D., Liew, A.C. & Chang, C.S. (1994). A neural network short-term load forecaster. Electric Power Systems Research, 28 (3), 227-234.
  36. Tang, Z., Fishwick, P.A. (1993). Feed forward neural nets as models for time series forecasting. Journal on Computing, 5 (4), 374-385.
  37. Valerio, L., Brano, G. C. & Mariavittoria D.F. (2014). Artificial Neural Networks to Predict the Power Output of a PV Panel. International Journal of Photoenergy, 2014, Article ID 193083, 1-12.
  38. Wong, F.S. (1991). Time series forecasting using back propagation neural networks. Neurocomputing, 2 (4), 147-159.
  39. Yegnanarayana, B. (2009). Artificial neural networks. New York. USA: PHI Learning Pvt. Ltd
  40. Zhang, G., Eddy, P. B. & Michael, Y. H. (1998). Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting, 14 (1), 35-62.
  41. Zhang, X., (1994). Time series analysis and prediction by neural networks. Optimization Methods and Software, 4(2), 151-170.
  42. Ziane, A., Necaibia, A., Sahouane, N., Dabou, R., Mostefaoui, M., Bouraiou, A., Khelifi, S., Rouabhia, A. & Blal, M. (2021). Photovoltaic output power performance assessment and forecasting: Impact of meteorological variables. Solar Energy, 220, 745-75.

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