Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

The Power Supplies Demand Conditions of the Big Data Technology Optimization Model

Authors
Fei Shi, Yonghuan Hu, Fengna Dong
Corresponding Author
Fei Shi
Available Online June 2017.
DOI
10.2991/caai-17.2017.33How to use a DOI?
Keywords
power supplies management; characteristics of supply and demand; big data technology; model optimization
Abstract

Power supplies timely supply for the stable operation of the national grid and construction has the vital role. In this article, through the analysis of the characteristics of the electricity supplies demand, from the perspective of demand side, the production side, supply side research the contradiction between supply and demand of power supplies, so it is concluded that current power supplies demand patterns are faced with the practical problems, and gives the power supplies are optimized by the big data technology demand patterns theory method.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
Series
Advances in Intelligent Systems Research
Publication Date
June 2017
ISBN
10.2991/caai-17.2017.33
ISSN
1951-6851
DOI
10.2991/caai-17.2017.33How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Fei Shi
AU  - Yonghuan Hu
AU  - Fengna Dong
PY  - 2017/06
DA  - 2017/06
TI  - The Power Supplies Demand Conditions of the Big Data Technology Optimization Model
BT  - Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)
PB  - Atlantis Press
SP  - 156
EP  - 159
SN  - 1951-6851
UR  - https://doi.org/10.2991/caai-17.2017.33
DO  - 10.2991/caai-17.2017.33
ID  - Shi2017/06
ER  -