Research on the Power Demand forecasting in Beijing-Tianjin-Tangshan Area Considering the Special Time Influence Based on Support Vector Machine Model

Zhonghua He; Tao Zhang; Fuqiang Li; Yuou Hu; Nana Li

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Research on the Power Demand forecasting in Beijing-Tianjin-Tangshan Area Considering the Special Time Influence Based on Support Vector Machine Model

机译：基于支持向量机模型的特殊时间影响，北京天津 - 唐山地区电力需求预测研究

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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.

机译：电力需求预测的准确性将直接影响电力系统的规划，安全性和稳定性。电力需求受到与经济发展有关的因素的大大影响。此外，特别活动也将对行业，服务业和居民的电力消耗产生影响。本文采用灰色关系分析和支持向量机智能算法来构建基于电力经济发展的滚动功率需求预测方法。通过考虑特殊时期对电力需求的影响，本文预测了北京，天津和唐山特殊时期的电力需求。最后，分析表明，2017年北京，天津和唐山的电力需求是33.37亿千瓦时。

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来源
《Software Engineering》 |2018年第1期|共5页
作者
Zhonghua He; Tao Zhang; Fuqiang Li; Yuou Hu; Nana Li;
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中图分类软件工程;
关键词
Beijing-Tianjin-Tang RegionSupport Vector MachinesSpecial PeriodPower Demand Forecasting;

机译：北京 - 天津唐牧场地区载体载体建设专业时期需求预测;

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Research on the Power Demand forecasting in Beijing-Tianjin-Tangshan Area Considering the Special Time Influence Based on Support Vector Machine Model

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