Availability predictions of solar power plants using multiple regression and neural networks: an analytical study

Authors

  • Kanak Saini
    Department of Mathematics & Statistics Manipal University Jaipur, Jaipur 303007, India
  • Monika Saini
    Department of Mathematics & Statistics Manipal University Jaipur, Jaipur 303007, India
  • Ashish Kumar
    Department of Mathematics & Statistics Manipal University Jaipur, Jaipur 303007, India
  • Dinesh Kumar Saini
    Department of IoT and Intelligent Systems Manipal University Jaipur, Jaipur-303007, India 

Keywords:

Solar PV Power Plant, Artificial Neural Networks, Regression Model, Markovian Approach

Abstract

This analysis aims to develop an efficient mathematical model for prediction of the system availability of a solar photovoltaic power plant under the concept of redundancy and exponentially distributed random variables. For this objective, a stochastic model of the photovoltaic power plant is created with the help of the Markov birth-death technique. It is assumed that all the repairs are perfect and random variables statistically independent. The predictive techniques, namely regression analysis and artificial neural networks are used to predict the availability of the PV power plant in different experimental setups with the help of SPSS software. The impact of failure and repair rates on the availability of the PV power plant investigated. Experimental data used to calculate the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of both predictive techniques. It is identified that the MAE and RMSE of the regression model are less in comparison to the ANN model. So, the regression model outperforms ANN in the performance prediction of PV power plants. The outcomes of this study may help design PV solar plants and plan maintenance strategies for solar PV power plants.

Dimensions

[1] P. S. Rajpal, K. S. Shishodia & G. S. Sekhon, “An artificial neural network for modeling reliability, availability and maintainability of a repairable system”, Reliability Engineering & System Safety 91 (2006) 809. https://doi.org/10.1016/j.ress.2005.08.004.

[2] B. Adenso-Diaz, C. Mena, S. Garcı́a-Carbajal & M. Liechty, “The impact of supply network characteristics on reliability”, Supply Chain Management: An International Journal 17 (2012) 263. https://doi.org/10.1108/13598541211227108.

[3] D. Bose, G. Gliosli, K. Maiidal, S. P. Sau & S. Kuuar, “Measurement and evaluation of reliability, availability and maintainability of a diesel locomotive engine”, International Journal of Scientific and Research Publications 3 (2013) 1. http://www.ijsrp.org/research-paper-0913/ijsrp-p21113.pdf.

[4] K. F. Nimon & F. L. Oswald, “Understanding the results of multiple linear regression: Beyond standardized regression coefficients”, Organizational Research Methods 16 (2013) 650. https://doi.org/10.1177/1094428113493929.

[5] O. Fink, E. Zio & U. Weidmann, “Predicting component reliability and level of degradation with complex-valued neural networks”, Reliability Engineering & System Safety 121 (2014) 198. https://doi.org/10.1016/j.ress.2013.08.004.

[6] B. Kaushik & H. Banka, “Performance evaluation of approximated artificial neural network (AANN) algorithm for reliability improvement”, Applied Soft Computing 26 (2015) 303. https://doi.org/10.1016/j.asoc.2014.10.002.

[7] Z. Ma, S. Zhu, H. N. Koutsopoulos & L. Ferreira, “Quantile regression analysis of transit travel time reliability with automatic vehicle location and farecard data”, Transportation Research Record 2652 (2017) 19. https://doi.org/10.3141/2652-03.

[8] R. B. Solanki, H. D. Kulkarni, S. Singh, A. K. Verma & P. V. Varde, “Optimization of regression model using principal component regression method in passive system reliability assessment”, Progress in Nuclear Energy 103 (2018) 126. https://doi.org/10.1016/j.pnucene.2017.11.012.

[9] J. Ferrero Bermejo, J. F. Gómez Fernández, F. Olivencia Polo & A. Crespo Márquez, “A review of the use of artificial neural network models for energy and reliability prediction. A study of the solar PV, hydraulic and wind energy sources”, Applied Sciences 9 (2019) 1844. https://doi.org/10.3390/app9091844.

[10] K. Wen, L. He, J. Liu & J. Gong, “An optimization of artificial neural network modeling methodology for the reliability assessment of corroding natural gas pipelines”, Journal of Loss Prevention in the Process Industries 60 (2019) 1. https://doi.org/10.1016/j.jlp.2019.03.010.

[11] D. Goyal, A. Kumar, M. Saini & H. Joshi, “Reliability, maintainability, and sensitivity analysis of physical processing unit of sewage treatment plant”, SN Applied Sciences 1 (2019) 1507. https://doi.org/10.1007/s42452-019-1544-7.

[12] A. Sayed, M. El-Shimy, M. El-Metwally & M. Elshahed, “Reliability, availability, and maintainability analysis for grid-connected solar photovoltaic systems”, Energies 12 (2019) 1213. https://doi.org/10.3390/en12071213.

[13] D. C. Jordan, B. Marion, C. Deline, T. Barnes & M. Bolinger, “PV field reliability status—Analysis of 100 000 solar systems”, Progress in Photovoltaics: Research and Applications 28 (2020) 739. https://doi.org/10.1002/pip.3262.

[14] A. Kumar, R. Singh, M. Saini & O. Dahiya, “Reliability, availability and maintainability analysis to improve the operational performance of soft water treatment and supply plant”, Journal of Engineering Science & Technology Review 13 (2020) 183. https://doi.org/10.1177/16878132221115931.

[15] A. S. Maihulla & I. Yusuf, “Performance analysis of photovoltaic systems using (RAMD) analysis”, Journal of the Nigerian Society of Physical Sciences 3 (2021) 172. https://doi.org/10.46481/jnsps.2021.194.

[16] P. Kumar, S. K. Singh & S. D. Choudhary, “Reliability prediction analysis of aspect-oriented application using soft computing techniques”, Materials Today: Proceedings 45 (2021) 2660. https://doi.org/10.1016/j.matpr.2020.11.518.

[17] D. Cevasco, S. Koukoura & A. J. Kolios, “Reliability, availability, maintainability data review for the identification of trends in offshore wind energy applications”, Renewable and Sustainable Energy Reviews 136 (2021) 110414. https://doi.org/10.1016/j.rser.2020.110414.

[18] A. S. Maihulla & I. Yusuf, “Reliability, availability, maintainability, and dependability analysis of photo-voltaic systems”, Life Cycle Reliability and Safety Engineering 11 (2022) 19. https://doi.org/10.1007/s41872-021-00180-1.

[19] G. Diamenu, J. C. Attachie & C. K. Amuzuvi, “Reliability analysis of wind energy generation system using stochastic method”, Journal of Power and Energy Engineering 10 (2022) 26. https://doi.org/10.4236/jpee.2022.108003.

[20] P. R. Sonawane, S. Bhandari, R. B. Patil & S. Al-Dahidi, “Reliability and criticality analysis of a large-scale solar photovoltaic system using fault tree analysis approach”, Sustainability 15 (2023) 4609. https://doi.org/10.3390/su15054609.

[21] H. Abdulla, A. Sleptchenko & A. Nayfeh, “Photovoltaic systems operation and maintenance: A review and future directions”, Renewable and Sustainable Energy Reviews 195 (2024) 114342. https://doi.org/10.1016/j.rser.2024.114342.

[22] M. P. Petronijevic, I. Radonjic, M. Dimitrijevic, L. Pantić & M. Calasan, “Performance evaluation of single-stage photovoltaic inverters under soiling conditions”, Ain Shams Engineering Journal 15 (2024) 102353. https://doi.org/10.1016/j.asej.2023.102353.

[23] L. Liu, Y. Xiao & J. Yang, “Daily optimization of maintenance routing and scheduling in a large-scale photovoltaic power plant with time-varying output power”, Applied Energy 360 (2024) 122793. https://doi.org/10.1016/j.apenergy.2024.122793.

[24] R. Pimpalkar, A. Sahu, A. Yadao & R. B. Patil, “Failure modes and effects analysis of polycrystalline photovoltaic modules exposed to the composite climate of India”. Journal of The Institution of Engineers (India): Series C, 105 (2024) 339. https://doi.org/10.1007/s40032-024-01027-1.

[25] P. R. Sonawane, S. Bhandari & R. B. Patil, “Reliability and criticality analysis of a large-scale solar photovoltaic system using fuzzy-fault tree analysis approach”, Journal of King Saud University-Engineering Sciences (2024). [Online]. https://doi.org/10.1016/j.jksues.2024.09.001.

[26] T. Orosz, A. Rass olkin, P. Arsénio, P. Poór, D. Valme & Á. Sleisz, “Current challenges in operation, performance, and maintenance of photovoltaic panels”, Energies 17 (2024) 1306. https://doi.org/10.3390/en17061306.

[27] P. Fitrianingtyas, M.K. Asy’ari, N. Elyawati & A. Musyafa, “Reliability, maintainability and availability analysis of solar power plant in Pantai Baru using voltage measurement data”, E3S Web of Conferences 479 (2024) 05001. https://doi.org/10.1051/e3sconf/202447905001.

[28] R. B. Patil, A. Khalkar, S. Al-Dahidi, R. S. Pimpalkar, S. Bhandari & M. Pecht, “A reliability and risk assessment of solar photovoltaic panels using a failure mode and effects analysis approach: a case study”, Sustainability 16 (2024) 4183. https://doi.org/10.3390/su16104183.

[29] G. O. Odekina, A. F. Adedotun & O. F. Imaga, “Modeling and forecasting the third wave of Covid-19 incidence rate in Nigeria using vector autoregressive model approach”, Journal of the Nigerian Society of Physical Sciences 4 (2022) 117. https://doi.org/10.46481/jnsps.2022.431.

[30] O. J. Ibidoja, F. P., Shan, J. Sulaiman & M.K. M. Ali, “Robust M-estimators and machine learning algorithms for improving the predictive accuracy of seaweed contaminated big data”, Journal of the Nigerian Society of Physical Sciences 5 (2023) 1137. https://doi.org/10.46481/jnsps.2023.1137.

[31] U. Hassan & M.T. Ismail, “Improving forecasting accuracy using quantile regression neural network combined with unrestricted mixed data sampling”, Journal of the Nigerian Society of Physical Sciences 5 (2023) 1394. https://doi.org/10.46481/jnsps.2023.1394.

[32] U. C. Obini, C. Jeremiah & S. A. Igwe, “Development of a machine learning based fileless malware filter system for cyber-security”, Journal of the Nigerian Society of Physical Sciences 6 (2024) 2192. https://doi.org/10.46481/jnsps.2024.2192.

Published

2025-05-01

How to Cite

Availability predictions of solar power plants using multiple regression and neural networks: an analytical study. (2025). Journal of the Nigerian Society of Physical Sciences, 7(2), 2398. https://doi.org/10.46481/jnsps.2025.2398

Issue

Volume 7, Issue 2, May 2025

Section

Mathematics & Statistics
Copyright & Licensing

Copyright (c) 2025 Kanak Saini, Monika Saini, Ashish Kumar, Dinesh Kumar Saini

Creative Commons License

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

How to Cite

Availability predictions of solar power plants using multiple regression and neural networks: an analytical study. (2025). Journal of the Nigerian Society of Physical Sciences, 7(2), 2398. https://doi.org/10.46481/jnsps.2025.2398

Similar Articles

121-130 of 308

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)