Traditional energy sources have become one of the most serious causes of environmental pollution because of the growing demand for energy. Because of the carbon emissions that have recently increased greatly, we had to search for a safe, cheap, and environmentally friendly energy source. Many photovoltaic (PV) solar panels are used as an energy source because of free and environmental friendliness. However, this technology has become a source of inspiration for many researchers. The proposed method suggests to extract useful features from PV and wind generators and then train the system to control them and update the inputs according to prediction results. Solar energy produces energy that varies according to the external influences and the immediate changes in weather conditions. Solar panels are connected through an inverter with the grid, through which the work of the solar panels is monitored using the Internet. It is worth using neural networks (NN) to control variables and adopt system output of previous iteration in processing operations. Use of deep learning (DL) in the control of solar energy panels helps reduce the direct surveillance of the system online. Solar power generation systems mainly depend on reducing the pollution resulting from carbon emissions. Saving CO₂ emission is the main purpose of PV panel cells, so smart monitoring can achieve better result in that case.

中文翻译：

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基于无线遥感模型的深度学习太阳系逆变器监测

由于对能源的需求不断增长，传统能源已成为环境污染最严重的原因之一。由于最近碳排放量大幅增加，我们不得不寻找安全、廉价、环保的能源。由于免费且环保，许多光伏 (PV) 太阳能电池板被用作能源。然而，这项技术已经成为许多研究人员的灵感来源。所提出的方法建议从光伏和风力发电机中提取有用的特征，然后训练系统控制它们并根据预测结果更新输入。太阳能产生的能量根据外部影响和天气条件的即时变化而变化。太阳能电池板通过逆变器与电网连接，通过它可以使用 Internet 监控太阳能电池板的工作。值得使用神经网络（NN）来控制变量并在处理操作中采用前一次迭代的系统输出。在太阳能电池板控制中使用深度学习 (DL) 有助于减少对系统在线的直接监视。太阳能发电系统主要依靠减少碳排放造成的污染。节约二氧化碳 太阳能发电系统主要依靠减少碳排放造成的污染。节约二氧化碳 太阳能发电系统主要依靠减少碳排放造成的污染。节约二氧化碳₂发射是光伏电池板的主要目的，因此在这种情况下，智能监控可以达到更好的效果。