MF-GCN-LSTM: a cloud-edge distributed framework for key positions prediction in grid projects

In this article, we solve the key positions prediction problem of engineering projects in smart grid, which pays more attention to the spatial-temporal distribution of projects. Many studies show that the projects are affected by multi-dimensional features such as time, space, correlation etc. Howev...

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Bibliographic Details
Published in:	Journal of cloud computing : advances, systems and applications Vol. 11; no. 1; pp. 1 - 14
Main Authors:	Huang, Shaoyuan, Zhang, Yuxi, Peng, Guozheng, Zhao, Juan, Zhu, Keping, Zhang, Heng, Wang, Xiaofei
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2022 Springer Nature B.V SpringerOpen
Subjects:	Accuracy Cloud computing Computer Communication Networks Computer Science Computer System Implementation Computer Systems Organization and Communication Networks Data analysis Empowering the Future Generation Cloud Computing with Internet of Things Experiments Feature extraction Graph Convolutional Networks Information Systems Applications (incl.Internet) Key Positions Prediction Multi-Dimensional Feature Extraction Open access publishing Optimization Project management Projects Networks Smart grid Software Engineering/Programming and Operating Systems Special Purpose and Application-Based Systems Temporal distribution Time series Projects Networks Key Positions Prediction Multi-Dimensional Feature Extraction Graph Convolutional Networks
ISSN:	2192-113X, 2192-113X
Online Access:	Get full text
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