The Efficiency Prediction of the Laser Charging Based on GA-BP
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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/9/3143 |
| _version_ | 1797505078064578560 |
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| author | Chengmin Wang Guangji Li Imran Ali Hongchao Zhang Han Tian Jian Lu |
| author_facet | Chengmin Wang Guangji Li Imran Ali Hongchao Zhang Han Tian Jian Lu |
| author_sort | Chengmin Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T04:13:27Z |
| format | Article |
| id | doaj.art-d264856c19274e1c80bd4c5d731ffd5a |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T04:13:27Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-d264856c19274e1c80bd4c5d731ffd5a2023-11-23T08:07:04ZengMDPI AGEnergies1996-10732022-04-01159314310.3390/en15093143The Efficiency Prediction of the Laser Charging Based on GA-BPChengmin Wang0Guangji Li1Imran Ali2Hongchao Zhang3Han Tian4Jian Lu5School of Science, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Science, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Science, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Science, Nanjing University of Science and Technology, Nanjing 210094, ChinaSchool of Digital Equipment, Jiangsu Vocational College of Electronics and Information, Huai’an 223003, ChinaSchool of Science, Nanjing University of Science and Technology, Nanjing 210094, ChinaIn 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.https://www.mdpi.com/1996-1073/15/9/3143wireless power transferlaser chargingBPGA |
| spellingShingle | Chengmin Wang Guangji Li Imran Ali Hongchao Zhang Han Tian Jian Lu The Efficiency Prediction of the Laser Charging Based on GA-BP Energies wireless power transfer laser charging BP GA |
| title | The Efficiency Prediction of the Laser Charging Based on GA-BP |
| title_full | The Efficiency Prediction of the Laser Charging Based on GA-BP |
| title_fullStr | The Efficiency Prediction of the Laser Charging Based on GA-BP |
| title_full_unstemmed | The Efficiency Prediction of the Laser Charging Based on GA-BP |
| title_short | The Efficiency Prediction of the Laser Charging Based on GA-BP |
| title_sort | efficiency prediction of the laser charging based on ga bp |
| topic | wireless power transfer laser charging BP GA |
| url | https://www.mdpi.com/1996-1073/15/9/3143 |
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