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Proceedings Paper

The research and application of the power big data
Author(s): Suxiang Zhang; Dong Zhang; Yaping Zhang; Jinping Cao; Huiming Xu
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Paper Abstract

Facing the increasing environment crisis, how to improve energy efficiency is the important problem. Power big data is main support tool to realize demand side management and response. With the promotion of smart power consumption, distributed clean energy and electric vehicles etc get wide application; meanwhile, the continuous development of the Internet of things technology, more applications access the endings in the grid power link, which leads to that a large number of electric terminal equipment, new energy access smart grid, and it will produce massive heterogeneous and multi-state electricity data. These data produce the power grid enterprise's precious wealth, as the power big data. How to transform it into valuable knowledge and effective operation becomes an important problem, it needs to interoperate in the smart grid. In this paper, we had researched the various applications of power big data and integrate the cloud computing and big data technology, which include electricity consumption online monitoring, the short-term power load forecasting and the analysis of the energy efficiency. Based on Hadoop, HBase and Hive etc., we realize the ETL and OLAP functions; and we also adopt the parallel computing framework to achieve the power load forecasting algorithms and propose a parallel locally weighted linear regression model; we study on energy efficiency rating model to comprehensive evaluate the level of energy consumption of electricity users, which allows users to understand their real-time energy consumption situation, adjust their electricity behavior to reduce energy consumption, it provides decision-making basis for the user. With an intelligent industrial park as example, this paper complete electricity management. Therefore, in the future, power big data will provide decision-making support tools for energy conservation and emissions reduction.

Paper Details

Date Published: 23 January 2017
PDF: 10 pages
Proc. SPIE 10322, Seventh International Conference on Electronics and Information Engineering, 103222I (23 January 2017); doi: 10.1117/12.2265486
Show Author Affiliations
Suxiang Zhang, State Grid Information & Telecommunication Branch (China)
Dong Zhang, State Power Economic Research Institute (China)
Yaping Zhang, State Grid Corporations of China (China)
Jinping Cao, State Grid Information & Telecommunication Branch (China)
Huiming Xu, State Grid Information & Telecommunication Branch (China)

Published in SPIE Proceedings Vol. 10322:
Seventh International Conference on Electronics and Information Engineering
Xiyuan Chen, Editor(s)

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