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5G is the latest-generation network that powers the IoT in pace with energy transformation essentially demanded during the modern world, especially the wireless form. Laser charging means laser beam irradiance of the solar cell panel to generate the photovoltaic power, and its way belongs to the WPT (Wireless Power Transfer). During the process, uncertain and ungovernable factors affect the energy transfer of efficiency. We have chosen the In0.3Ga0.7As material as the semiconductor of the PV (photovoltaic) panel to enhance its photoelectric conversion efficiency. Nevertheless, during the PV processing, nonlinearity elements impact the prediction modelling, making it extremely difficult. This paper proposes a new forecasting model based on SVM (Support Vector Machine) which is drawn from SLT (Statistical Learning Theory). Since the forecast belongs to classification issue essentially, the main idea of SVM is extending the SLT dimensions. We used the kernel function for realization. The PV is done under the different conditions (constant temperature, gradient temperature, or various laser power). The gathered data is processed by MATLAB and the 3rd software: LIBSVM. Without the parameter optimization, the prediction accuracy of classification is 80% (32/40) against 100% (40/40). Considering that the experiment data have small quantity, further make the over 650 data size samples which are calculated by the formula to verify the model. Through the effect of a few random factors, the result still remains 80% or higher. The research work has resulted in a solution of a fast and precise prediction model in laser charging solar cell panel. It also revealed the implicit relationship between the factors of the PV. |
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