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Fog computing uses Radial Basis Function for Power Production Classification Solar Panel Electricity

机译:雾计算采用径向基函数供电分类太阳能电池板电力

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Energy is a very important problem for life, especially electrical energy in areas that are still lagging behind and far from development activities. These problems encourage the creation of an innovation that can be used to help people get the availability of electricity easily. Solar panels is effective energy producers, but many things are disrupt to productivity. Therefore, a system is needed to support the sustainability of the energy sources produced. Monitoring requires an online computing system that requires the availability of large broadband on the internet network. This is a serious obstacle if you want an online realtime system productivity monitoring system. In this study we propose fog computing technology using the support vector machines method for computational processes. Data on temperature, humidity, intensity and power were obtained from physical parameter sensors of solar panels. And then, The data is computed using the support vector machines method with the kernel radial base function in the fog network. This research produces an information system is capable to provide an early warning to the user to display the results of electric power production that is available in the field online and real time. Root mean square error showed very good results which reached 0.066.
机译:能量是生命的一个非常重要的问题,特别是在仍然滞后的地区和远离发展活动的地区的电能。这些问题鼓励创造创新,这些创新可以用来帮助人们轻松获得电力的可用性。太阳能电池板是有效的能源生产商,但许多事情都扰乱了生产力。因此,需要一种系统来支持所产生的能源的可持续性。监控需要一个在线计算系统,该系统需要在互联网网络上进行大型宽带的可用性。如果您想要在线实时系统生产力监控系统,这是一个严重障碍。在这项研究中,我们使用支持向量机方法来提出雾计算技术进行计算过程。从太阳能电池板的物理参数传感器获得温度,湿度,强度和功率的数据。然后,使用具有雾网络中的内核径向基本功能的支持向量机方法计算数据。该研究产生了一种信息系统,能够向用户提供预警,以显示在线和实时现场提供的电力生产结果。根均方误差显示出非常好的结果,达到0.066。

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