Big Data-based the Smart Grid Application Analysis
International Journal of Industrial and Manufacturing Systems Engineering
Volume 4, Issue 4, July 2019, Pages: 41-47
Received: Oct. 4, 2019; Accepted: Oct. 21, 2019; Published: Oct. 25, 2019
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Zhengguang Liu, College of Water Resources and Architectural Engineering, Northwest A & F University, Yangling, China
Qiaoyu Liu, College of Water Resources and Architectural Engineering, Northwest A & F University, Yangling, China
Mengjiang Wu, Electrical Department, College of Science and Technology of China Three Gorges University, Yichang City, China
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The smart grid is the future development direction of the power industry. The ultimate goal of the smart grid should be to build a real-time monitoring system covering the entire production process of the power system, including power generation, transmission, power transmission, power distribution, and power scheduling. A smart grid is the development trend of the future power industry, and the application analysis of the smart grid is the basis for ensuring economic and safe operation. Smart Grid Application Analysis (SGAA) based on big data, is of considerable significance to the development of the power system. Based on the comprehensive comparison of domestic and foreign literature, this paper puts forward the prediction application of "Big Data +" and makes a simple evaluation of the possible potential power-side load and regulator prediction model of new energy development. It also introduces the shortcomings in the current stage, as well as the critical technologies of the big data industry that need to be developed urgently. The smart grid is the future direction of power industry development, but the current stage of the development of related technology is not enough, this paper gives suggestions for the development of smart grid and big data.
Industrial Applications, Big Data, Smart Grids
To cite this article
Zhengguang Liu, Qiaoyu Liu, Mengjiang Wu, Big Data-based the Smart Grid Application Analysis, International Journal of Industrial and Manufacturing Systems Engineering. Vol. 4, No. 4, 2019, pp. 41-47. doi: 10.11648/j.ijimse.20190404.12
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