智能电网
微电网
计算机科学
边缘计算
分布式计算
服务器
GSM演进的增强数据速率
互联网
可再生能源
实时计算
计算机网络
人工智能
工程类
电气工程
万维网
控制(管理)
作者
Lingling Lv,Zhenyu Wu,Lei Zhang,Brij B. Gupta,Zhihong Tian
出处
期刊:IEEE Transactions on Industrial Informatics
[Institute of Electrical and Electronics Engineers]
日期:2022-11-01
卷期号:18 (11): 7946-7954
被引量:56
标识
DOI:10.1109/tii.2022.3163137
摘要
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.
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