Comparaison of solar cell photocurrent by solar tracker using an Arduino card and the machine learning algorithm

Authors

  • EL HADI CHAHID LRPSI-Laboratory, Polydisciplinary Faculty, Sultan Moulay Slimane University, Beni Mellal, Morocco
  • Elhassan ELJAOUI Higher School of education and training Sultan Moulay Slimane University, Beni Mellal, Morocco.
  • Mohamed DRIOUCH Research Laboratory in Physics and Sciences for Engineers (LRPSI), Polydisciplinary Faculty, Sultan Moulay Slimane University, Beni Mellal, Morocco.
  • Soufiane BELHOUIDEG Research Laboratory in Physics and Sciences for Engineers (LRPSI), Polydisciplinary Faculty, Sultan Moulay Slimane University, Beni Mellal, Morocco.

Keywords:

Arduino card, Solar tracker, Photovoltaic cell, Servomotor, Solar energy, Artificial intelligence

Abstract

The purpose of this study is to analyze the realization of a solar tracker based on the Arduino card, and on the other hand, to discuss the prediction of future solar cell photo-current generated by the machine learning algorithm. Firstly, the system creats the photocurrent Iph  based on programming in Arduino software the movement of the solar panel at predefined time intervals (between sunrise and sunset) in accordance with the path of the sun during the day, so as to keep the active surface of the panel perpendicular to the solar radiation. Finally, we discuss the prediction of solar cell photo-current generated by the machine learning algorithm. The result shows that the random forest algorithm is more accurate compared to the K-Nearest Neighbors, decision tree algorithms based on the RMSE statistical indicator.

Downloads

Download data is not yet available.

References

MA. Green, Y. Hishikawa, W. Warta, et al., “Solar cell efficiency,” Progr Photovolt Res Appl. Vol.28, pp.3-15, 2020.

T. Tudorache, L. Kreindler, “ Design of a Solar Tracker System for PV Power Plants, ” Acta Polytech. Hung, vol.7(1), pp.23-39, 2010.

A. Mohamad et al , “Review of analysis on vertical and horizontal axis wind turbines”, Appl. Mech. Mater, vol. 695, pp. 753-756, 2015.

B. Sujatha, “Optimization and Performance Evaluation of Single Axis Arduino Solar Tracker”, Int J Recent Innovation Trends Computing Comm, vol. 6 (4), pp. 16-20, 2018.

S. Russell and P. Norvig, Artificiel intelligence: a modern approach, 4th edition, Pearson, 2020.

S. Dick, “Artificiel intelligence,” Harvard Data Science Review, 2019, pp 1–8.

B. Pradhan, A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS, Comput. Geosci, Vol.51, pp. 350-365, 2013.

G. Saporta, Probabilités, analyse des données et statistique. Editions Technip, 2006.

R.A. Nugrahaeni, & K. Mutijarsa, Comparative analysis of machine

learning KNN, SVM, and random forests algorithm for facial expression classification. In 2016 International Seminar on Application for

Technology of Information and Communication (ISemantic) (pp. 163-168). IEEE (2016, August).

M. Mahendran et al, An experimental comparison study between single-axis tracking and fixed photovoltaic solar panel efficiency and power output: Case study in east coast Malaysia, Sustainable Development Conference (Bangkok, Thailand), 2013.

L. Salgado-Conrado, A review on sun position sensors used in solar applications , Renew Sustain Energy Rev, vol.82 (3), pp.2128-2146, 2018.

R. G. Vieira, Comparative performance analysis between static solar panels and single-axis tracking system on a hot climate region near to the equator, Volume 64, Pages 672-681, October 2016.

S. A. Jumaat et al, Horizontal Single Axis Solar Tracker Using Arduino Approach, Indones. J. Electr. Eng. Comput. Sci, Vol.12 (2), pp.489-496, 2018.

Downloads

Published

2022-10-03

How to Cite

[1]
E. H. CHAHID, E. . ELJAOUI, M. . DRIOUCH, and S. . BELHOUIDEG, “Comparaison of solar cell photocurrent by solar tracker using an Arduino card and the machine learning algorithm”, IJCEDS, vol. 2, no. 3, Oct. 2022.

Issue

Vol. 2 No. 3 (2022): Volume 2. Issue 3

Section

Articles

License

Copyright (c) 2022 EL HADI CHAHID, Elhassan ELJAOUI, Mohamed DRIOUCH, Soufiane BELHOUIDEG

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Copyright on any article published in the International Journal of Computer Engineering and Data Science (IJCEDS) is retained by the author(s). All articles are published under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0), which permits any non-commercial use, distribution, and reproduction in any medium, provided that the original work is properly cited.

License Agreement

By submitting and publishing their work in IJCEDS, the authors:

  • Grant IJCEDS the non-exclusive right to publish the article and to identify IJCEDS as the original publisher.

  • Authorize any third party to use, share, and reproduce the article for non-commercial purposes, provided that appropriate credit is given to the original authors and source, and a link to the license is included.