10.57647/j.mjee.2025.1902.36

Optimizing Photovoltaic Power Prediction Using Computational Methods and Artificial Neural Networks

PDF
  • Cempaka Amalin Mahadzir1,
  • Ahmad Fateh Mohamad Nor*1,
  • Siti Amely Jumaat1,
  • Noor Syahirah Ahmad Safawi2,
  1. Green and Sustainable Energy Focus Group, Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia (UTHM), Batu Pahat, Johor, Malaysia
  2. Hyper E-Mech Sdn Bhd, Johor Bahru, Malaysia

Received: 0025-01-07

Revised: 2025-03-03

Accepted: 2025-03-24

Published in Issue 2025-06-01

Copyright (c) 2025 Cempaka Amalin Mahadzir, Ahmad Fateh Mohamad Nor, Siti Amely Jumaat, Noor Syahirah Ahmad Safawi (Author)

Creative Commons License

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

How to Cite

Mahadzir, C. A. ., Nor, A. F. M. ., Jumaat, S. A. ., & Safawi, N. S. A. . (2025). Optimizing Photovoltaic Power Prediction Using Computational Methods and Artificial Neural Networks. Majlesi Journal of Electrical Engineering, 19(2 (June 2025). https://doi.org/10.57647/j.mjee.2025.1902.36
Crossref
Scopus
Google Scholar
Europe PMC

PDF views: 104

Abstract

This paper focuses on utilizing an Artificial Neural Network (ANN) to predict photovoltaic (PV) panel output power. Since solar power output is fluctuating and depends on climatic, geographical and temporal factors, precise prediction requires the implementation of computational approaches. The aim of this research is to develop ANN algorithms that anticipate solar power output and enhance the structure of them by incorporating the derating factor due to dirt (kdirt) into account. The effectiveness and dependability of the ANN are determined using MATLAB software. By comparing the Mean Squared Error (MSE) of four different values of derating factor due to dirt which are 0.8, 0.88, 0.9 and 0.98 in ANN predictions comprehend with 4 input layers and 10 hidden layers. Direct data input is obtained through a photovoltaic solar panel at Universiti Tun Hussein Onn Malaysia (UTHM). Comparative analysis also has been carried out after the results has been obtained from the mathematical equations. The daily solar power output predictions are effectively achieved by the deployed ANN. As the result, the optimal kdirt has been selected which is 0.8 based on its ability to produce the most accurate ANN predictions than the other values of kdirt.

Keywords

  • Power prediction,
  • Solar output,
  • ANN,
  • MSE,
  • Derating factor
PDF

References

  1. Kumar, Sunil, and Kavita Rathore. "Renewable Energy for Sustainable Development Goal of Clean and Affordable Energy." International Journal of Materials Manufacturing and Sustainable Technologies, 2 (1), 1–15. DOI: https://doi.org/10.56896/IJMMST.2023.2.1.001
  2. Yekinni, Sanusi, et al. “Solar Photovoltaic Energy System’’. Nanogenerators and Self-Powered Systems, IntechOpen, 24 May 2023. DOI: 10.5772/intechopen.108958
  3. Kashani, Marziyeh, et al. "The Optimization of Photovoltaic Systems Design Using Mathematical Modeling and QFD-DSM Methods." Majlesi Journal of Electrical Engineering 16.2 (2022): 55-72. https://doi.org/10.30486/mjee.2022.696495
  4. Seda.gov.my. (2020). FiT – Renewable Energy Malaysia. [online].
  5. Obaideen, Khaled, et al. "Solar energy: Applications, trends analysis, bibliometric analysis and research contribution to sustainable development goals (SDGs)." Sustainability 15.2 (2023): 1418. https://doi.org/10.3390/su15021418
  6. Putri, Raihan, et al. "Design and Implementation of a Solar Power System on Grid SDN 023905 BIN JAI using PVSYST Software." Majlesi Journal of Electrical Engineering 17.3 (2023). https://doi.org/10.30486/mjee.2023.1989935.1167
  7. Setyawati, Harsasi, et al. "Photovoltaic Performance of Naphthol Blue Black Complexes and their Band Gap Energy." International Journal of Nanoelectronics and Materials 16.2 (2023): 335-344 https://doi.org/10.58915/ijneam.v16i2.1232
  8. Ameur, Arechkik, et al. "Forecast modeling and performance assessment of solar PV systems." Journal of Cleaner Production 267 (2020): 122167. https://doi.org/10.1016/j.jclepro.2020.122167
  9. Mishra, Vijay Laxmi, Yogesh K. Chauhan, and Kripa S. Verma. "Various modeling approaches of photovoltaic module: A comparative analysis." Majlesi Journal of Electrical Engineering 17.2 (2023): 117-131. 10.30486/mjee.2023.1984023.1109
  10. Nawab, Faisal, et al. "Solar irradiation prediction using empirical and artificial intelligence methods: A comparative review." Heliyon (2023). https://doi.org/10.1016/j.heliyon.2023.e17038
  11. Jamil, Wan Juzaili, et al. "Modeling of soiling derating factor in determining photovoltaic outputs." IEEE Journal of Photovoltaics 10.5 (2020): 1417-1423. https://doi.org/10.1109/JPHOTOV.2020.3003815
  12. Liu, Shicheng, Ruidong Chang, Jian Zuo, Ronald J. Webber, Feng Xiong, and Na Dong. "Application of artificial neural networks in construction management: Current status and future directions." Applied Sciences 11, no. 20 (2021): 9616. https://doi.org/10.3390/app11209616
  13. Mahadzir, Cempaka Amalin, Ahmad Fateh Mohamad Nor, and Siti Amely Jumaat. "Photovoltaic Power Output Prediction using Graphical User Interface and Artificial Neural Network." Majlesi Journal of Electrical Engineering 17.4 (2023): 73-78. https://doi.org/10.30486/mjee.2023.1995210.1243
  14. Nawab, Faisal, et al. "Solar irradiation prediction using empirical and artificial intelligence methods: A comparative review." Heliyon (2023). https://doi.org/10.1016/j.heliyon.2023.e17038
  15. Sustainable Energy Development Authority (SEDA MALAYSIA), “Fundamentals of Solar Photovoltaics Technology”. Chapter 5: Photovoltaic Technology, page 77. (2022). (ISBN 978-967-12390-7-0).
  16. Franklin, E. A. (2019). “Calculations for a Grid-Connected Solar Energy System.” University of Arizona Cooperative Extension: Tucson, AZ, USA, 2-6.
  17. Sara, Umme, Morium Akter, and Mohammad Shorif Uddin. "Image quality assessment through FSIM, SSIM, MSE and PSNR—a comparative study." Journal of Computer and Communications 7.3 (2019): 8-18. https://doi.org/10.4236/jcc.2019.73002
  18. Mayer, M. J., & Gróf, G. (2021). “Extensive comparison of physical models for photovoltaic power forecasting.” Applied Energy, 283, 116239. https://doi.org/10.1016/j.apenergy.2020.116239