Towards Edge Computing Based Distributed Data Analytics Framework in Smart Grids

CORC > 沈阳自动化研究所 > 中国科学院沈阳自动化研究所 > 工业控制网络与系统研究室

	Towards Edge Computing Based Distributed Data Analytics Framework in Smart Grids
	Zeng P(曾鹏)1,2 ; Wang ZF(王忠锋)1,2 ; Huang, Xu ; Li T(李桐); Song CH(宋纯贺)1,2
	2019
会议日期	July 26-28, 2019
会议地点	New York city, NY, United states
页码	283-292
英文摘要	Edge computing, as an emerging paradigm empower the network edge devices with intelligence, has become a prominent and promising future for Internet of things. Meanwhile, machine learning method, especially deep learning method has experience tremendous success recently in many application scenario. Recently, deep learning method applied in IoT scenario is also explored in many literatures. However, how to combine edge computing and deep learning method to advance the data analytics in smart grids has not been fully studied. To this end, in this paper, we propose ECNN (Edge-deployed Convolution Neural Network) in edge computing assisted smart grids to greatly enhance the ability in data aggregation and analytics. We also discuss how to train such network in edge computing distributively. Experiments shows the advantage of our paradigm.
产权排序	1
会议录	Artificial Intelligence and Security - 5th International Conference, ICAIS 2019, Proceedings
会议录出版者	Springer Verlag
会议录出版地	Berlin
语种	英语
ISSN号	0302-9743
ISBN号	978-3-030-24264-0
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/25395]
专题	沈阳自动化研究所_工业控制网络与系统研究室
通讯作者	Song CH(宋纯贺)
作者单位	1.Liaoning Electric Power Research Institute, State Grid Liaoning Electric Power Co., Ltd., Shenyang 110000, China; Shenyang Power Supply Company, State Grid Liaoning Electric Power Co., Ltd., Shenyang 110000, China 2.Chinese Academy of Sciences, Shenyang Institute of automation, Shenyang 110016, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China;
推荐引用方式 GB/T 7714	Zeng P,Wang ZF,Huang, Xu,et al. Towards Edge Computing Based Distributed Data Analytics Framework in Smart Grids[C]. 见:. New York city, NY, United states. July 26-28, 2019.