Prediction of Voltage Generation in Triboelectric ‎Nanogenerator Using Machine Learning Algorithms

  • Authors

    • Deepa A Associate Professor, Department of ECE, Gopalan College of Engineering and Management, Karnataka
    • Loganathan Nachimuthu Senior Lecturer, College of Engineering and Technology, Engineering Department, ‎University of Technology and Applied Sciences- Nizwa, Sultanate of Oman
    • Kavitha MV Associate Professor, Cambridge Institute of Technology, Electronics and Communication, Bangalore
    • Jyothi D Assistant Professor, Department of ECE, Gopalan College of Engineering and Management, Karnataka
    https://doi.org/10.14419/1r9nsq10

    Received date: May 6, 2025

    Accepted date: May 17, 2025

    Published date: June 10, 2025

  • Triboelectric Nanogenerator (TENG); Voltage Prediction; RMSE (Root Mean Square Error); MAPE (Mean Absolute Percentage Error; Key ‎Words or Phrases in Alphabetical Order; Separated by Semicolon

  • Abstract

    The rapid evolution of solar panels towards greener energy has paved the way for eco-friendly renewable energy generation. However, the effective management of disposed solar cells is an important factor to consider in reducing adverse environmental and health consequences. Hence, ‎the novel based Triboelectric Nano generators are fabricated from waste solar cells and waste chocolate wrappers. The TENG harnesses ‎frictional energy from the contact between the materials, converting it into useful electrical power. This innovative system promotes the ‎efficient utilization of discarded resources, contributing to both renewable energy generation and waste reduction. As a result, the current work offers a realistic technique for gathering electricity and represents a major step in mitigating the difficulties associated with disposing of solar cell waste. The ‎output voltage generation by the TENG is predicted using various Machine learning algorithms. The predictive model performance is also ‎analyzed through various metrics such as Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE)‎.

  • References

    1. Muhammad Wajahat, Abbas Z. Kouzani, Sui Yang Khoo, M. A. Parvez Mahmud, ‘Development of Triboelectric Nanogenerators Using Novel 3D Printed Polymer Materials’ advanced engineering materials in 2023. https://doi.org/10.1002/adem.202301897.
    2. Sk Masum Nawaz, Mainak Saha, Nayim Sepay, Abhijit Mallik, ‘Energy-from-waste: A triboelectric nanogenerator fabricated from waste polysty-rene for energy harvesting and self-powered sensor’, nano energy 104 in 2022. https://doi.org/10.1016/j.nanoen.2022.107902.
    3. Rumana Farheen Sagade Muktar Ahmed, Sebghatullah Amini, Sangamesha Modanahalli Ankanathappa and Krishnaveni Sannathammegowda, ‘Electricity out of electronic trash: Triboelectric nanogenerators from discarded smartphone displays for biomechanical energy harvesting’, waste management, volume 178, ppt 1-11, 2024 https://doi.org/10.1016/j.wasman.2024.02.009.
    4. Muzamil Hussain Memon, Maria Mustafa, Zeeshan Ali Abro, ‘Fabrication of low-cost and environmental-friendly EHD printable thin film nano-composite triboelectric nanogenerator using household recyclable materials’, Mehran University Research Journal Of Engineering & Technology, Vol 43, 2024 https://doi.org/10.22581/muet1982.2401.2873.
    5. A. Sharma, P. Mahajan & R. Garg, ‘End-of-life solar photovoltaic panel waste management in India: forecasting and environmental impact assess-ment’, International Journal of environmental science and technology, Volume 21, pp 1961-1980. https://doi.org/10.1007/s13762-023-04953-2.
    6. Muhammad Umaid Bukhari et al., ‘Waste to energy: Facile, low-cost and environment-friendly triboelectric nanogenerators using recycled plastic and electronic wastes for self-powered portable electronics’, Energy Reports 8 (2022), pp 1687–1695. https://doi.org/10.1016/j.egyr.2021.12.072.
    7. F, Hussain Z, Numan M, FaƟma B, Najam ul Haq M, Majeed S, Ahmad T (2022) Triboelectric Nanogenerator Based on PTFE PlasƟc Waste BoƩle and Aluminum Foil . Materials Innovations, 2(8), 203-213. https://doi.org/10.54738/MI.2022.2803.
    8. Trinh, V.-L.; Chung, C.-K. Advances in Triboelectric Nanogenerators for Sustainable and Renewable Energy: Working Mechanism, Tribo-Surface Structure, Energy Storage-Collection System, and Applications. Processes 2023. https://doi.org/10.3390/pr11092796.
    9. Geetha, A., Santhakumar, J., Sundaram, K. M., Usha, S., Thentral, T. M., Boopathi, C. S., & Sathyamurthy, R. (2022). Prediction of hourly solar radiation in Tamil Nadu using ANN model with different learning algorithms. Energy Reports, 8, 664-671. https://doi.org/10.1016/j.egyr.2021.11.190.
    10. K. M. Sundaram, A. Hussain, P. Sanjeevikumar, J. B. Holm-Nielsen, V. K. Kaliappan and B. K. Santhoshi, "Deep Learning for Fault Diagnostics in Bearings, Insulators, PV Panels, Power Lines, and Electric Vehicle Applications—The State-of-the-Art Approaches," in IEEE Access, vol. 9, pp. 41246-41260, 2021, https://doi.org/10.1109/ACCESS.2021.3064360.
    11. Sugavanam, K. R., Jeyabharath, R., & Veena, P. (2021). Convolutional Neural Network-based harmonic mitigation technique for an adaptive shunt active power filter. https://doi.org/10.1080/00051144.2021.1985703.
    12. Renyun Zhang, Machine learning-assisted triboelectric nanogenerator-based self-powered sensors, Cell Reports Physical Science, Volume 5, Issue 4,2024. https://doi.org/10.1016/j.xcrp.2024.101888.
    13. Jianxiong Zhu, Shanling Ji, Jiachuan Yu, Haoran Shao, Haiying Wen, Hui Zhang, Zhijie Xia, Zhisheng Zhang, Chengkuo Lee, Machine learning-augmented wearable triboelectric human-machine interface in motion identification and virtual reality, Nano Energy, Volume 103, Part A,2022. https://doi.org/10.1016/j.nanoen.2022.107766.
    14. Jie Xu, Lingrong Kong, Yu Wang, Haodong Hong, Using deep learning and an annular triboelectric sensor for monitoring downhole motor rotor faults, Nano Energy, Volume 133,2025. https://doi.org/10.1016/j.nanoen.2024.110478.
    15. Su, Yifeng, Dezhi Yin, Xinmao Zhao, Tong Hu, and Long Liu. 2025. "Exploration of Advanced Applications of Triboelectric Nanogenerator-Based Self-Powered Sensors in the Era of Artificial Intelligence" Sensors 25, no. 8: 2520. https://doi.org/10.3390/s25082520.
    16. Zhang, B.; Jiang, Y.; Ren, T.; Chen, B.; Zhang, R.; Mao, Y. Recent advances in nature inspired triboelectric nanogenerators for self-powered sys-tems. Int. J. Extrem. Manuf. 2024. https://doi.org/10.1088/2631-7990/ad65cc.

  • Downloads

  • How to Cite

    A, D. ., Nachimuthu, L. ., MV, K. ., & D, J. (2025). Prediction of Voltage Generation in Triboelectric ‎Nanogenerator Using Machine Learning Algorithms. International Journal of Basic and Applied Sciences, 14(2), 145-150. https://doi.org/10.14419/1r9nsq10