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2000
Volume 15, Issue 1
  • ISSN: 2210-3279
  • E-ISSN: 2210-3287

Abstract

Introduction

Solar energy is a crucial component and contributes a large portion of renewable resources, the demand for which has recently increased. During previous years, it was difficult to predict the amount of energy obtained from photovoltaic systems. The angle of inclination of the solar panel, the amount of radiation, the speed of the wind, the amount of humidity, and the temperature of the weather are major factors that effectively affect the production of electrical energy. Scientists have used many strategies to predict the power generated by PV modules accurately, but each method has different pros and cons.

Methods

This study tested three training algorithms for artificial neural networks (ANN): scaled conjugate gradient (SCG), Levenberg Marquardt (LM), and Bayesian regularization (BR), determining which one performed best in terms of prediction speed and accuracy. Twenty-eight thousand two hundred ninety-six samples of experimental data for primary influencing environmental factors were fed into the artificial neural network, which consists of 15 hidden layers. Before training the network, we preprocessed the data to remove factors that have a secondary effect.

Results

The analytical results showed that the artificial neural network trained according to the LM algorithm is the best in terms of accuracy and speed of predicting the resulting photovoltaic energy.

Conclusion

The results showed that although the regression evaluation and MSE values for the LM, SCG, and BR algorithms are close (98.129%, 0.0622, 97.849%, 0.0587, 98.151%, and 0.0585, respectively), the LM training algorithm is the best in terms of speed of calculation and display of results.

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2024-09-18
2025-05-13
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References

  1. Ascencio-VásquezJ. Photovoltaic power forecasting methodsIntech202110.5772/intechopen.97049
    [Google Scholar]
  2. NageemR. RJ. Predicting the power output of a grid-connected solar panel using multi-input support vector regression.Procedia Comput. Sci.201711572373010.1016/j.procs.2017.09.143
    [Google Scholar]
  3. GerberD.L. LiouR. BrownR. Energy-saving opportunities of direct-DC loads in buildings.Appl. Energy201924827428710.1016/j.apenergy.2019.04.089
    [Google Scholar]
  4. CotaposM. Mitigation control against partial shading effects in large-scale photovoltaic power plants using an improved forecasting technique.2017
    [Google Scholar]
  5. FarayolaA.M. SunY. AliA. Optimization of PV systems using linear interactions regression MPPT techniques.2018 IEEE PES/IAS PowerAfrica.201854555010.1109/PowerAfrica.2018.8521064
    [Google Scholar]
  6. KangJ.G. KimJ.H. KimJ.T. Performance evaluation of DSC windows for buildings.Int. J. Photoenergy201320131610.1155/2013/472086
    [Google Scholar]
  7. AsdrubaliF. CotanaF. MessineoA. On the evaluation of solar greenhouse ef-ciency in building simulation during the heating period.Energies2012561864188010.3390/en5061864
    [Google Scholar]
  8. TanY.T. KirschenD.S. Impact on the Power System of a Large Penetration of Photovoltaic Generation.2007 IEEE Power Engineering Society General MeetingTampa, FL, USA, 2007, pp. 1-8.10.1109/PES.2007.385563
    [Google Scholar]
  9. JumaatS.A. Prediction of photovoltaic (PV) output using arti_cial neutral network (ANN) based on ambient factors.J. Phys. Conf.20181049
    [Google Scholar]
  10. SaberianH. Modelling and prediction of photovoltaic power output using articial neural networks.Int. J. Photoenergy2014201411010.1155/2014/469701
    [Google Scholar]
  11. LuH. ZhangY. HaoP. MaJ. ZhongH. GuT. YangM. ZhangL. Output performance prediction of PV modules based on power-law model from manufacturer datasheet.J. Renew. Sustain. Energy202214303350110.1063/5.0088190
    [Google Scholar]
  12. BimenyimanaS. NorenseG. AsemotaO. LinglingL. LiL. Output power prediction of photovoltaic module using nonlinear autoregressive neural network.J. Energy, Environ. Chem. Eng.20172324010.11648/j.jeece.20170204.11
    [Google Scholar]
  13. DandilE. GurgenE. Prediction of photovoltaic panel power output using arti-cial neural networks learned by heuristic algorithms: A comparative studyProc. Int. Conf. Comput. Sci. Eng. (UBMK)201739740210.1109/UBMK.2017.8093423
    [Google Scholar]
  14. KhanS. ShaikhF. SiddiquiM.M. HussainT. KumarL. NaharA. Hourly forecasting of solar photovoltaic power in Pakistan using recurrent neural networks.Int. J. Photoenergy2022202211110.1155/2022/7015818
    [Google Scholar]
  15. BaaranK. Systematic literature review of photovoltaic output power forecasting.IET Renew. Power Gener.2020141939613973
    [Google Scholar]
  16. ElamimB. Generation of photovoltaic output power forecast using arti-cial neural networks.Advanced Intelligent Systems for Sustainable Development (Lecture Notes in Electrical Engineering). EzziyyaniM. Cham, SwitzerlandSpringer2020624
    [Google Scholar]
  17. KhanM.A. KhanM.A. AliH. AshrafB. KhanS. BaigD-E-Z. WadoodA. KhurshaidT. Output power prediction of a photovoltaic module through artificial neural network.IEEE Access20221011616011616610.1109/ACCESS.2022.3216384
    [Google Scholar]
  18. SalimM.S. SabriN. DheyabA.A. ANN-Based Formulation of Path Loss Prediction for Radio Wave Propagation for Indoor Agriculture and Sensor Networks.Nanotechnol. Percept.20242024404414
    [Google Scholar]
  19. NaoumiS. BazziA. BomfinR. ChafiiM. Complex Neural Network based Joint AoA and AoD Estimation for Bistatic ISAC.IEEE J. Sel. Top. Signal Process.202411510.1109/JSTSP.2024.3387299
    [Google Scholar]
  20. LinG.Q. LiL.L. TsengM.L. LiuH.M. YuanD.D. TanR.R. An improved moth-flame optimization algorithm for support vector machine prediction of photovoltaic power generation.J. Clean. Prod.202025311996610.1016/j.jclepro.2020.119966
    [Google Scholar]
  21. JainR. Enhance traffic flow prediction with real-time vehicle data integration.J. auton. intell.20236210.32629/jai.v6i2.574
    [Google Scholar]
  22. Lo BranoV. CiullaG. Di FalcoM. Arti-cial neural networks to predict the power output of a PV panel.Int. J. Photoenergy2014201411210.1155/2014/193083
    [Google Scholar]
  23. AlaloulW. S. QureshiA. H. Data Processing Using Artificial Neural NetworksIntechopen2020
    [Google Scholar]
  24. YaoT. WangJ. WuH. PVOD v1.0: A photovoltaic power output dataset.2021
    [Google Scholar]
  25. KayriM. Predictive Abilities of Bayesian Regularization and Levenberg–Marquardt Algorithms in Artificial Neural Networks: A Comparative Empirical Study on Social Data.Mathematical and Computational Applications2016212011110.3390/mca21020020
    [Google Scholar]
  26. Guide, Matlab User’S. Neural network toolbox.MathWorks 402002522
    [Google Scholar]
  27. OkutH. GianolaD. RosaG.J.M. WeigelK.A. Prediction of body mass index in mice using dense molecular markers and a regularized neural network.Genet. Res.201193318920110.1017/S001667231000066221481292
    [Google Scholar]
  28. BishopC.M. TippingM.E. A hierarchical latent variable model for data visualization.IEEE Trans. Pattern Anal. Mach. Intell.199820328129310.1109/34.667885
    [Google Scholar]
  29. BurdenF. WinklerD. Bayesian regularization of neural networks.Methods Mol Biol.20094582544
    [Google Scholar]
  30. MarwalaT. Bayesian training of neural networks using genetic programming.Pattern Recognit. Lett.200728121452145810.1016/j.patrec.2007.03.004
    [Google Scholar]
  31. TitteringtonD.M. Bayesian methods for neural networks and related models.Stat. Sci.200419112813910.1214/088342304000000099
    [Google Scholar]
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