Proceedings of the 2017 7th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2017)

On the Research of the Smart Grid Load Forecasting Cloud Platform Architecture

Authors
Mingyue Zhai
Corresponding Author
Mingyue Zhai
Available Online December 2017.
DOI
https://doi.org/10.2991/mcei-17.2017.115How to use a DOI?
Keywords
Load forecasting; Cloud platform; Load forecasting method; Smart grid; Smart meter
Abstract
Load forecasting is making calculations or predictions of a future event or condition based on analysis or study of historical data, events or observations. With the development of smart grid, smart meter is also increasing largely, which can generate metering data per 15 mins, and there comes a huge amount of metering data. Thus such huge amount of data leads to some challenges in data processing. This paper introduces the meaning of studying load forecasting, and recommends current relevant research. Then, the cloud platform architecture, the key technologies based cloud and current relevant research for load forecasting based on cloud in smart grid is discussed. Finally it introduces the related load forecasting services based on the cloud platform architecture in smart grid. After all, there is a conclusion of this paper.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2017 7th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2017)
Part of series
Advances in Computer Science Research
Publication Date
December 2017
ISBN
978-94-6252-430-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/mcei-17.2017.115How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Mingyue Zhai
PY  - 2017/12
DA  - 2017/12
TI  - On the Research of the Smart Grid Load Forecasting Cloud Platform Architecture
BT  - 2017 7th International Conference on Mechatronics, Computer and Education Informationization (MCEI 2017)
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/mcei-17.2017.115
DO  - https://doi.org/10.2991/mcei-17.2017.115
ID  - Zhai2017/12
ER  -