Kangzhen Zheng
ABSTRACT
The construction of energy Internet can realize the massive collection of load-side data, and the local utilization of load-side data through edge computing devices can effectively reduce the pressure of cloud computing and communication system, but the computing and solving ability of edge devices is worse than that of cloud, and too many computing tasks of edge devices may also affect the overall scheduling. Therefore, this paper combines big data mining technology to analyze the data analysis and decision support in the cyber-physical systems of energy Internet, and studies the optimal scheduling method of energy Internet in cloud edge environment around the multi-energy complementary process and accurate demand response of energy Internet. The research results show that the energy Internet information model based on information physical fusion takes into account the plug-and-play characteristics of distributed devices in the network, and at the same time reflects the multi-time scale characteristics of energy Internet, taking into account the autonomy of specific devices in the network and so on. From the experimental evaluation, it can be seen that big data mining has a good effect on data analysis and decision support in energy Internet cyber-physical systems.
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