Analysis of cross platform power data governance

Jiachun Chen, Zhaogong Zhang


With the rapid development of smart grid, how to solve the problems of professional business cooperation and information
sharing, long data input time, accurate data, weak real-time, data extraction, redundant storage, low quality, privacy protection, further
comprehensive management of data, mining the value of data resources has become one of the important tasks for the development of
electric power enterprises. The traditional method uses edge computing for data transmission and task allocation. On this basis, we study
the cross-platform power governance scheme based on edge unloading computing and deep reinforcement learning. The fi nal experimental
results show that the scheme has smaller delay and lower energy consumption.


Edge computing; Smart grid; Deep reinforcement learning; Power data governance

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