| Issue |
MATEC Web Conf.
Volume 139, 2017
2017 3rd International Conference on Mechanical, Electronic and Information Technology Engineering (ICMITE 2017)
|
|
|---|---|---|
| Article Number | 00217 | |
| Number of page(s) | 4 | |
| DOI | https://doi.org/10.1051/matecconf/201713900217 | |
| Published online | 05 December 2017 | |
Technical Research on the Electric Power Big Data Platform of Smart Grid
1 State Grid Sichuan Economic Research Institute SGSERI Chengdu, China
2 College of Management Science, Chengdu University of Technology, Chengdu, China
* Corresponding author: luckfunny_wang@163.com
Through elaborating on the associated relationship among electric power big data, cloud computing and smart grid, this paper put forward general framework of electric power big data platform based on the smart grid. The general framework of the platform is divided into five layers, namely data source layer, data integration and storage layer, data processing and scheduling layer, data analysis layer and application layer. This paper makes in-depth exploration and studies the integrated management technology based on big data, the index technology based on electric power big data, the analysis technology based on electric power big data and the excavation technology based on electric power big data, to achieve the breakthrough of key technologies of electric power big data. Finally, the typical case of big data applications shows that the key technology application of electric power big data platform will bring changes for the power enterprises, which has demonstration meaning and important reference meaning.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.