IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i13p3529-d1694567.html
   My bibliography  Save this article

Flat vs. Curved: Machine Learning Classification of Flexible PV Panel Geometries

Author

Listed:
  • Ahmad Manasrah

    (Mechanical Engineering Department, Al-Zaytoonah University of Jordan, Amman P.O. Box 11733, Jordan)

  • Yousef Jaradat

    (Electrical Engineering Department, Al-Zaytoonah University of Jordan, Amman P.O. Box 11733, Jordan)

  • Mohammad Masoud

    (Electrical Engineering Department, Al-Zaytoonah University of Jordan, Amman P.O. Box 11733, Jordan)

  • Mohammad Alia

    (Cybersecurity Department, Al-Zaytoonah University of Jordan, Amman P.O. Box 11733, Jordan)

  • Khaled Suwais

    (Faculty of Computer Studies, Arab Open University, Riyadh 11681, Saudi Arabia)

  • Piero Bevilacqua

    (Department of Mechanical, Energetic and Management Engineering, University of Calabria, 87036 Arcavacata, Italy)

Abstract

As the global demand for clean and sustainable energy grows, photovoltaics (PVs) have become an important technology in this industry. Thin-film and flexible PV modules offer noticeable advantages for irregular surface mounts and mobile applications. This study investigates the use of four machine learning models to detect different flexible PV module geometries based on power output data. Three identical flexible PV modules were mounted in flat, concave, and convex configurations and connected to batteries via solar chargers. The experimental results showed that all geometries fully charged their batteries within 6–7 h on a sunny day with the flat, concave-, and convex-shaped modules achieving a peak power of 95 W. On a cloudy day, the concave and convex modules recorded peak outputs of 72 W and 65 W, respectively. Simulation results showed that the XGBoost model delivered the best classification performance, showing 93% precision with the flat-mounted module and 98% recall across all geometries. In comparison, the KAN model recorded the lowest precision (78%) with the curved geometries. A calibration analysis on the ML models showed that Random Forest and XGBoost were well calibrated for the flat-mounted module. However, they also showed overconfidence and underconfidence issues with the curved module geometries.

Suggested Citation

  • Ahmad Manasrah & Yousef Jaradat & Mohammad Masoud & Mohammad Alia & Khaled Suwais & Piero Bevilacqua, 2025. "Flat vs. Curved: Machine Learning Classification of Flexible PV Panel Geometries," Energies, MDPI, vol. 18(13), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3529-:d:1694567
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/13/3529/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/13/3529/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Francesco Nicoletti & Mario Antonio Cucumo & Vittorio Ferraro & Dimitrios Kaliakatsos & Albino Gigliotti, 2022. "A Thermal Model to Estimate PV Electrical Power and Temperature Profile along Panel Thickness," Energies, MDPI, vol. 15(20), pages 1-17, October.
    2. Akhter, Muhammad Naveed & Mekhilef, Saad & Mokhlis, Hazlie & Ali, Raza & Usama, Muhammad & Muhammad, Munir Azam & Khairuddin, Anis Salwa Mohd, 2022. "A hybrid deep learning method for an hour ahead power output forecasting of three different photovoltaic systems," Applied Energy, Elsevier, vol. 307(C).
    3. Muhammad Naveed Akhter & Saad Mekhilef & Hazlie Mokhlis & Ziyad M. Almohaimeed & Munir Azam Muhammad & Anis Salwa Mohd Khairuddin & Rizwan Akram & Muhammad Majid Hussain, 2022. "An Hour-Ahead PV Power Forecasting Method Based on an RNN-LSTM Model for Three Different PV Plants," Energies, MDPI, vol. 15(6), pages 1-21, March.
    4. Bowen Zhou & Xinyu Chen & Guangdi Li & Peng Gu & Jing Huang & Bo Yang, 2023. "XGBoost–SFS and Double Nested Stacking Ensemble Model for Photovoltaic Power Forecasting under Variable Weather Conditions," Sustainability, MDPI, vol. 15(17), pages 1-24, September.
    5. Wang, Zhenlong & Wang, Yifan & Zhang, Xinrui & Yang, Dong & Ma, Duanyu & Ramakrishna, Seeram & Yuan, Weizheng & Ye, Tao, 2024. "Flexible photovoltaic micro-power system enabled with a customized MPPT," Applied Energy, Elsevier, vol. 367(C).
    6. Francesco Nicoletti & Piero Bevilacqua, 2024. "Hourly Photovoltaic Production Prediction Using Numerical Weather Data and Neural Networks for Solar Energy Decision Support," Energies, MDPI, vol. 17(2), pages 1-22, January.
    7. Mina Nezamisavojbolaghi & Erfan Davodian & Amal Bouich & Mouhaydine Tlemçani & Oumaima Mesbahi & Fernando M. Janeiro, 2023. "The Impact of Dust Deposition on PV Panels’ Efficiency and Mitigation Solutions: Review Article," Energies, MDPI, vol. 16(24), pages 1-19, December.
    8. 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.
    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. Bevilacqua, Piero & Bruno, Roberto & Rollo, Antonino & Ferraro, Vittorio, 2022. "A novel thermal model for PV panels with back surface spray cooling," Energy, Elsevier, vol. 255(C).
    2. Xilong Lin & Yisen Niu & Zixuan Yan & Lianglin Zou & Ping Tang & Jifeng Song, 2024. "Hybrid Photovoltaic Output Forecasting Model with Temporal Convolutional Network Using Maximal Information Coefficient and White Shark Optimizer," Sustainability, MDPI, vol. 16(14), pages 1-20, July.
    3. Qian He & Mingbin Zhao & Shujie Li & Xuefang Li & Zuoxun Wang, 2025. "Machine Learning Prediction of Photovoltaic Hydrogen Production Capacity Using Long Short-Term Memory Model," Energies, MDPI, vol. 18(3), pages 1-17, January.
    4. Mehmet Das & Erhan Arslan & Sule Kaya & Bilal Alatas & Ebru Akpinar & Burcu Özsoy, 2024. "Performance Evaluation of Photovoltaic Panels in Extreme Environments: A Machine Learning Approach on Horseshoe Island, Antarctica," Sustainability, MDPI, vol. 17(1), pages 1-34, December.
    5. Chen, Bin & He, Guo & Hu, Lin & Li, Heng & Wang, Miaoben & Zhang, Rui & Gao, Kai, 2025. "Energy management of electric vehicles based on improved long short term memory network and data-enabled predictive control," Applied Energy, Elsevier, vol. 384(C).
    6. Zheng, Xidong & Bai, Feifei & Zeng, Ziyang & Jin, Tao, 2024. "A new methodology to improve wind power prediction accuracy considering power quality disturbance dimension reduction and elimination," Energy, Elsevier, vol. 287(C).
    7. Jiahui Wang & Mingsheng Jia & Shishi Li & Kang Chen & Cheng Zhang & Xiuyu Song & Qianxi Zhang, 2024. "Short-Term Power-Generation Prediction of High Humidity Island Photovoltaic Power Station Based on a Deep Hybrid Model," Sustainability, MDPI, vol. 16(7), pages 1-24, March.
    8. Weiping Zhao & Shuai Hu & Zhiguang Dong, 2025. "Impact of Multiple Factors on Temperature Distribution and Output Performance in Dusty Photovoltaic Modules: Implications for Sustainable Solar Energy," Energies, MDPI, vol. 18(13), pages 1-17, June.
    9. Wang, Min & Rao, Congjun & Xiao, Xinping & Hu, Zhuo & Goh, Mark, 2024. "Efficient shrinkage temporal convolutional network model for photovoltaic power prediction," Energy, Elsevier, vol. 297(C).
    10. Krzysztof Pytel & Wiktor Hudy & Roman Filipek & Malgorzata Piaskowska-Silarska & Jana Depešová & Robert Sito & Ewa Janiszewska & Izabela Sieradzka & Krzysztof Sulkowski, 2025. "Evaluation of Environmental Factors Influencing Photovoltaic System Efficiency Under Real-World Conditions," Energies, MDPI, vol. 18(8), pages 1-32, April.
    11. Sun, Fengpeng & Li, Longhao & Bian, Dunxin & Bian, Wenlin & Wang, Qinghong & Wang, Shuang, 2025. "Photovoltaic power prediction based on multi-scale photovoltaic power fluctuation characteristics and multi-channel LSTM prediction models," Renewable Energy, Elsevier, vol. 246(C).
    12. Zhu, Jianhua & He, Yaoyao, 2025. "A novel hybrid model based on evolving multi-quantile long and short-term memory neural network for ultra-short-term probabilistic forecasting of photovoltaic power," Applied Energy, Elsevier, vol. 377(PC).
    13. Răzvan-Andrei Polcovnicu & Sebastian-Valeriu Hudișteanu & Nicolae Ţăranu & Dragoș Ungureanu & Marius Alexa & Iuliana Hudișteanu & Cătălin Onuțu & Alexandru-Florin Mustiață, 2025. "Experimental Investigation of Wind Effect on Roof Configurations with Photovoltaic Panel Systems for Sustainable Building Design," Sustainability, MDPI, vol. 17(10), pages 1-21, May.
    14. Zhao, Yang & Wang, Feng & Xu, Zipeng & Cheng, Chao & Gao, Dan & Zhang, Heng & Wang, Yuting, 2025. "Experimental and numerical investigation of spray cooling based photovoltaic/thermal system: Achieving high performance, low cost, and lightweight design," Energy, Elsevier, vol. 323(C).
    15. Chen, Di & Peng, Qiuzhi & Lu, Jiating & Huang, Peiyi & Song, Yufei & Peng, Fengcan, 2024. "Classification and segmentation of five photovoltaic types based on instance segmentation for generating more refined photovoltaic data," Applied Energy, Elsevier, vol. 376(PB).
    16. Abuzaid, Haneen & Awad, Mahmoud & Shamayleh, Abdulrahim & Alshraideh, Hussam, 2025. "Predictive modeling of photovoltaic system cleaning schedules using machine learning techniques," Renewable Energy, Elsevier, vol. 239(C).
    17. Yu, Hanxin & Chen, Shanlin & Chu, Yinghao & Li, Mengying & Ding, Yueming & Cui, Rongxi & Zhao, Xin, 2024. "Self-attention mechanism to enhance the generalizability of data-driven time-series prediction: A case study of intra-hour power forecasting of urban distributed photovoltaic systems," Applied Energy, Elsevier, vol. 374(C).
    18. Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.
    19. Putri Nor Liyana Mohamad Radzi & Muhammad Naveed Akhter & Saad Mekhilef & Noraisyah Mohamed Shah, 2023. "Review on the Application of Photovoltaic Forecasting Using Machine Learning for Very Short- to Long-Term Forecasting," Sustainability, MDPI, vol. 15(4), pages 1-21, February.
    20. Yusuf A Sha’aban, 2024. "Predictive models for short-term load forecasting in the UK’s electrical grid," PLOS ONE, Public Library of Science, vol. 19(4), pages 1-23, 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:3529-:d:1694567. 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.