Volume 6, Issue 1, March 2018, Page: 7-11
Research on the Power Demand forecasting in Beijing-Tianjin-Tangshan Area Considering the Special Time Influence Based on Support Vector Machine Model
Zhonghua He, North China Branch of State Grid Corporation of China, Beijing, China
Tao Zhang, North China Branch of State Grid Corporation of China, Beijing, China
Fuqiang Li, North China Branch of State Grid Corporation of China, Beijing, China
Yuou Hu, North China Branch of State Grid Corporation of China, Beijing, China
Nana Li, State Grid Energy Research Institute Limited Company, Beijing, China
Received: Dec. 13, 2017;       Accepted: Jan. 6, 2018;       Published: Jan. 19, 2018
DOI: 10.11648/j.se.20180601.12      View  1325      Downloads  57
Abstract
The accuracy of the power demand forecast will directly affect the planning, safety and stability of the power system. The power demand is greatly affected by the factors related to economic development. In addition, special events will also have impacts on the electricity consumption of industries, service industries and residents. In this paper, gray relational analysis and support vector machine intelligent algorithm are used to build a rolling power demand forecasting method based on the development of power economy. By considering the impact of special periods on power demand, this paper forecasts the power demand for special period in Beijing, Tianjin and Tangshan. Finally, the analysis shows that the electricity demand in Beijing, Tianjin and Tangshan in 2017 is 3,337 billion kwh.
Keywords
Beijing-Tianjin-Tang Region, Support Vector Machines, Special Period, Power Demand Forecasting
To cite this article
Zhonghua He, Tao Zhang, Fuqiang Li, Yuou Hu, Nana Li, Research on the Power Demand forecasting in Beijing-Tianjin-Tangshan Area Considering the Special Time Influence Based on Support Vector Machine Model, Software Engineering. Vol. 6, No. 1, 2018, pp. 7-11. doi: 10.11648/j.se.20180601.12
Copyright
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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