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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Vydané v:Journal of cloud computing : advances, systems and applications Ročník 11; číslo 1; s. 1 - 14
Hlavní autori: Huang, Shaoyuan, Zhang, Yuxi, Peng, Guozheng, Zhao, Juan, Zhu, Keping, Zhang, Heng, Wang, Xiaofei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2022
Springer Nature B.V
SpringerOpen
Predmet:
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
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Popis
Shrnutí: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. However, few work can accurately predict the key positions of projects based on multi-dimensional features. In order to solve this problem, we propose the idea of multi-feature extraction, and make use of the real-world records trace to conduct multi-dimensional modeling. Then we introduce a multi-dimensional features extraction model: Multi-Feature-based GCN-LSTM (MF-GCN-LSTM) to take the effect of time, space and correlation for predicting the key positions of projects. Experiments on different datasets with various project types have proved that our model can complete the key positions prediction task efficiently. Compared with the other traditional method and non-linear models, our model shows higher prediction accuracy and robustness. Moreover, we show that the whole prediction framework MF-GCN-LSTM can be split and deployed in a distributed manner to accelerate the inference of the model under the cloud edge system.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2192-113X
2192-113X
DOI:10.1186/s13677-022-00310-9