IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i13p3458-d1692133.html
   My bibliography  Save this article

Using Machine Learning and Analytical Modeling to Predict Poly-Crystalline PV Performance in Jordan

Author

Listed:
  • Sinan S. Faouri

    (Mechanical and Industrial Engineering Department, Applied Science Private University, Amman 11937, Jordan)

  • Salah Abdallah

    (Mechanical and Industrial Engineering Department, Applied Science Private University, Amman 11937, Jordan
    Retired.)

  • Dana Helmi Salameh

    (Mechanical and Industrial Engineering Department, Applied Science Private University, Amman 11937, Jordan)

Abstract

This study investigates the performance prediction of poly-crystalline photovoltaic (PV) systems in Jordan using experimental data, analytical models, and machine learning approaches. Two 5 kWp grid-connected PV systems at Applied Science Private University in Amman were analyzed: one south-oriented and another east–west (EW)-oriented. Both systems are fixed at an 11° tilt angle. Linear regression, Least Absolute Shrinkage and Selection Operator (LASSO), ElasticNet, and artificial neural networks (ANNs) were employed for performance prediction. Among these, linear regression outperformed the others due to its accuracy, interpretability, and computational efficiency, making it an effective baseline model. LASSO and ElasticNet were also explored for their regularization benefits in managing feature relevance and correlation. ANNs were utilized to capture complex nonlinear relationships, but their performance was limited, likely because of the small sample size and lack of temporal dynamics. Regularization and architecture choices are discussed in this paper. For the EW system, linear regression predicted an annual yield of 1510.45 kWh/kWp with a 2.1% error, compared to 1433.9 kWh/kWp analytically (3.12% error). The south-oriented system achieved 1658.15 kWh/kWp with a 1.5% error, outperforming its analytical estimate of 1772.9 kWh/kWp (7.89% error). Productivity gains for the south-facing system reached 23.64% (analytical), 10.43% (experimental), and 9.77% (predicted). These findings support the technical and economic assessment of poly-crystalline PV deployment in Jordan and regions with similar climatic conditions.

Suggested Citation

  • Sinan S. Faouri & Salah Abdallah & Dana Helmi Salameh, 2025. "Using Machine Learning and Analytical Modeling to Predict Poly-Crystalline PV Performance in Jordan," Energies, MDPI, vol. 18(13), pages 1-22, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3458-:d:1692133
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/13/3458/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/13/3458/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhaoxuan Li & SM Mahbobur Rahman & Rolando Vega & Bing Dong, 2016. "A Hierarchical Approach Using Machine Learning Methods in Solar Photovoltaic Energy Production Forecasting," Energies, MDPI, vol. 9(1), pages 1-12, January.
    2. Tarek Berghout & Mohamed Benbouzid & Toufik Bentrcia & Xiandong Ma & Siniša Djurović & Leïla-Hayet Mouss, 2021. "Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects," Energies, MDPI, vol. 14(19), pages 1-24, October.
    3. Maen Takruri & Maissa Farhat & Oscar Barambones & José Antonio Ramos-Hernanz & Mohammed Jawdat Turkieh & Mohammed Badawi & Hanin AlZoubi & Maswood Abdus Sakur, 2020. "Maximum Power Point Tracking of PV System Based on Machine Learning," Energies, MDPI, vol. 13(3), pages 1-14, February.
    4. Amith Khandakar & Muhammad E. H. Chowdhury & Monzure- Khoda Kazi & Kamel Benhmed & Farid Touati & Mohammed Al-Hitmi & Antonio Jr S. P. Gonzales, 2019. "Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar," Energies, MDPI, vol. 12(14), pages 1-19, July.
    5. Edalati, Saeed & Ameri, Mehran & Iranmanesh, Masoud, 2015. "Comparative performance investigation of mono- and poly-crystalline silicon photovoltaic modules for use in grid-connected photovoltaic systems in dry climates," Applied Energy, Elsevier, vol. 160(C), pages 255-265.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mahmoud Dhimish & Pavlos I. Lazaridis, 2022. "Approximating Shading Ratio Using the Total-Sky Imaging System: An Application for Photovoltaic Systems," Energies, MDPI, vol. 15(21), pages 1-16, November.
    2. Seyed Mahdi Miraftabzadeh & Cristian Giovanni Colombo & Michela Longo & Federica Foiadelli, 2023. "A Day-Ahead Photovoltaic Power Prediction via Transfer Learning and Deep Neural Networks," Forecasting, MDPI, vol. 5(1), pages 1-16, February.
    3. Chih-Chiang Wei, 2019. "Evaluation of Photovoltaic Power Generation by Using Deep Learning in Solar Panels Installed in Buildings," Energies, MDPI, vol. 12(18), pages 1-18, September.
    4. Yujing Sun & Fei Wang & Bo Wang & Qifang Chen & N.A. Engerer & Zengqiang Mi, 2016. "Correlation Feature Selection and Mutual Information Theory Based Quantitative Research on Meteorological Impact Factors of Module Temperature for Solar Photovoltaic Systems," Energies, MDPI, vol. 10(1), pages 1-20, December.
    5. Ahmad Manasrah & Mohammad Masoud & Yousef Jaradat & Piero Bevilacqua, 2022. "Investigation of a Real-Time Dynamic Model for a PV Cooling System," Energies, MDPI, vol. 15(5), pages 1-15, March.
    6. Woo-Gyun Shin & Ju-Young Shin & Hye-Mi Hwang & Chi-Hong Park & Suk-Whan Ko, 2022. "Power Generation Prediction of Building-Integrated Photovoltaic System with Colored Modules Using Machine Learning," Energies, MDPI, vol. 15(7), pages 1-17, April.
    7. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    8. Mariz B. Arias & Sungwoo Bae, 2020. "Design Models for Power Flow Management of a Grid-Connected Solar Photovoltaic System with Energy Storage System," Energies, MDPI, vol. 13(9), pages 1-14, April.
    9. Zoltan Varga & Ervin Racz, 2022. "Machine Learning Analysis on the Performance of Dye-Sensitized Solar Cell—Thermoelectric Generator Hybrid System," Energies, MDPI, vol. 15(19), pages 1-18, October.
    10. Xiao, Zenan & Huang, Xiaoqiao & Liu, Jun & Li, Chengli & Tai, Yonghang, 2023. "A novel method based on time series ensemble model for hourly photovoltaic power prediction," Energy, Elsevier, vol. 276(C).
    11. Chikh, Madjid & Berkane, Smain & Mahrane, Achour & Sellami, Rabah & Yassaa, Noureddine, 2021. "Performance assessment of a 400 kWp multi- technology photovoltaic grid-connected pilot plant in arid region of Algeria," Renewable Energy, Elsevier, vol. 172(C), pages 488-501.
    12. Maolin Cheng & Jiano Li & Yun Liu & Bin Liu, 2020. "Forecasting Clean Energy Consumption in China by 2025: Using Improved Grey Model GM (1, N)," Sustainability, MDPI, vol. 12(2), pages 1-20, January.
    13. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
    14. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    15. Azhar Ahmed Mohammed & Zeyar Aung, 2016. "Ensemble Learning Approach for Probabilistic Forecasting of Solar Power Generation," Energies, MDPI, vol. 9(12), pages 1-17, December.
    16. Jelke Wibbeke & Payam Teimourzadeh Baboli & Sebastian Rohjans, 2022. "Optimal Data Reduction of Training Data in Machine Learning-Based Modelling: A Multidimensional Bin Packing Approach," Energies, MDPI, vol. 15(9), pages 1-13, April.
    17. Maksymilian Mądziel, 2023. "Liquified Petroleum Gas-Fuelled Vehicle CO 2 Emission Modelling Based on Portable Emission Measurement System, On-Board Diagnostics Data, and Gradient-Boosting Machine Learning," Energies, MDPI, vol. 16(6), pages 1-15, March.
    18. Atsu, Divine & Seres, Istvan & Farkas, Istvan, 2021. "The state of solar PV and performance analysis of different PV technologies grid-connected installations in Hungary," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    19. Adolfo Crespo Márquez & Antonio de la Fuente Carmona & Sara Antomarioni, 2019. "A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency," Energies, MDPI, vol. 12(18), pages 1-25, September.
    20. Zulfiqar Ali & Syed Zagam Abbas & Anzar Mahmood & Syed Wajahat Ali & Syed Bilal Javed & Chun-Lien Su, 2023. "A Study of a Generalized Photovoltaic System with MPPT Using Perturb and Observer Algorithms under Varying Conditions," Energies, MDPI, vol. 16(9), pages 1-21, April.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3458-:d:1692133. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.