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Smart Grid 2.0 is the energy Internet based on advanced metering infrastructure and distributed systems that require an instantaneous two-way flow of energy information. Edge computing benefits from its proximity to the servers and edge nodes of the smart grid distributed systems, which can provide efficient and low latency information transmission to the smart grid. With the massive number of Internet of Things being used, the amount of real-time power usage information generated by that represents a huge challenge for edge computing. To improve the efficiency of information transmission and processing in power systems, this article combines different deep learning algorithms with edge computing to analyze and process distributed renewable energy generation and consumer power data in smart microgrid. Experiments on two real-world datasets from China and Belgium show that the proposed framework can obtain satisfactory prediction accuracy compared to existing approaches.