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The Efficiency Prediction of the Laser Charging Based on GA-BP

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  • Chengmin Wang

    (School of Science, Nanjing University of Science and Technology, Nanjing 210094, China
    School of Digital Equipment, Jiangsu Vocational College of Electronics and Information, Huai’an 223003, China)

  • Guangji Li

    (School of Science, Nanjing University of Science and Technology, Nanjing 210094, China)

  • Imran Ali

    (School of Science, Nanjing University of Science and Technology, Nanjing 210094, China
    Department of Physics, University of Agriculture, Faisalabad 38040, Pakistan)

  • Hongchao Zhang

    (School of Science, Nanjing University of Science and Technology, Nanjing 210094, China)

  • Han Tian

    (School of Digital Equipment, Jiangsu Vocational College of Electronics and Information, Huai’an 223003, China)

  • Jian Lu

    (School of Science, Nanjing University of Science and Technology, Nanjing 210094, China)

Abstract

In IoT applications, energy supply, especially wireless power transfer (WPT), has attracted the most attention in the relevant literature. In this paper, we present a new approach to laser irradiance solar cell panels and predicting energy transfer efficiency. From the previous experimental datasets, it has been discovered that in the laser charging (LC) process, temperature has a great impact on the efficiency, which is highly correlated with the laser intensity. Then, based on artificial neural network (ANN), we set the above temperature and laser intensity as inputs, and the efficiency as output through back propagation (BP) algorithm, and use neural network and BP to train and modify the network parameters to approach the real efficiency value. We also propose the genetic algorithm (GA) to optimize the learning rate of the BP, which achieved slightly superior results. The results of the experiment indicate that the prediction method reaches a high accuracy of about 99.4%. The research results in this paper provide an improved solution for the LC application, particularly the energy supply of IoT devices or small electronic devices through WPT.

Suggested Citation

  • Chengmin Wang & Guangji Li & Imran Ali & Hongchao Zhang & Han Tian & Jian Lu, 2022. "The Efficiency Prediction of the Laser Charging Based on GA-BP," Energies, MDPI, vol. 15(9), pages 1-12, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3143-:d:801893
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    References listed on IDEAS

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    1. Das, Utpal Kumar & Tey, Kok Soon & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Idris, Moh Yamani Idna & Van Deventer, Willem & Horan, Bend & Stojcevski, Alex, 2018. "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 912-928.
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