Overview of Application of Deep Learning With Image Data and Spatio-temporal Data of Power Grid

被引：0
作者：
Zhang Y. [1 ]
Qiu R. [1 ]
Yang F. [1 ]
Xu S. [1 ]
Shi X. [1 ]
He X. [1 ]
机构：
[1] Research Center for Big Data and Artificial Intelligence Engineering and Technologies, Shanghai Jiao Tong University, Minhang District, Shanghai
来源：
Dianwang Jishu/Power System Technology | 2019年 / 43卷 / 06期
基金：
中国国家自然科学基金;
关键词：
Big data; Deep learning; Image data; Smart grid; Temporal-spatial data;
D O I：
10.13335/j.1000-3673.pst.2018.2848
中图分类号：
学科分类号：
摘要：
Digitalization construction of smart grid provides huge amount of data, and development of deep learning provides an effective means for data value extraction. Firstly, this paper presents the history of deep learning and its basic framework, and summarizes its theoretical basis and technical system. Then, aiming at the actual demands of power system, and focusing on two main types of data, so called the image data and the temporal-spatial data, the application fields and values of deep learning are reviewed. Finally, the technical proposals for future development of deep learning in smart grid are put forward. © 2019, Power System Technology Press. All right reserved.
引用
收藏
页码：1865 / 1873
页数：8
相关论文
共 52 条
[1]  
Xue Y., Lai Y., Integration of macro energy thinking and big data thinking part two applications and explorations, Automation of Electric Power Systems, 40, 8, pp. 1-13, (2016)
[2]  
He X., Ai Q., Qiu R.C., Et al., A big data architecture design for smart grids based on random matrix theory, IEEE Transactions on Smart Grid, 8, 2, pp. 674-686, (2017)
[3]  
Hinton, Salakhutdinov, Reduciing the dimensionality of data with neural networks, Science, 313, 5786, pp. 504-507, (2006)
[4]  
Krizhevsky A., Sutskever I., Hinton G., Image net classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, 25, 2, pp. 33-42, (2012)
[5]  
Lecun Y.L., Bottou L., Bengio Y., Et al., Gradient-based learning applied to document recognition, Proceedings of the IEEE, 86, 11, pp. 2278-2324, (1998)
[6]  
Litjens G., Kooi T., Bejnordi B.E., Et al., A survey on deep learning in medical image analysis, Medical Image Analysis, 42, 9, pp. 60-88, (2017)
[7]  
Hochreiter S., Schmidhuber J., Long short-term memory, Neural Computation, 9, 8, pp. 1735-1780, (1997)
[8]  
Zhang Y., Qiu C., He X., Et al., A short-term load forecasting based on LSTM neural network, Electric Power Information and Communication Technology, 15, 9, pp. 19-25, (2017)
[9]  
Yu D., Shen J., Wang J., Et al., Research and application of helicopter in patrol and hotline operating maintenance of power lines, Power System Technology, 33, 6, pp. 107-112, (2009)
[10]  
Tong W., Yuan J., Li B., Application of image processing in patrol inspection of overhead transmission line by helicopter, Power System Technology, 34, 12, pp. 204-208, (2010)
123456