冀北地区年度最大负荷的支持向量机建模预测
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作者单位:

1.国网冀北电力有限公司经济技术研究院;2.华北电力大学经济与管理学院

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TM715

基金项目:

国家自然科学基金项目(71471059)和高等学校学科创新引智计划资助项目(B18021)资助


Research on Modeling Forecast of Annual Maximum Load in Northern Hebei Province Based on Support Vector Machine
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Affiliation:

1.Research Institute of Economics and Technology,State Grid Jibei Electric Power Company;2.School of Economic and Management,North China Electric Power University

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    摘要:

    冀北电力肩负着保障首都供电安全、服务冀北地区经济社会发展和服务国家新能源发展的特殊使命。本文在分析影响负荷变化的外部环境的前提下,使用支持向量机(support vector machine,SVM)和误差反向传播算法(back propagation,BP)神经网络对冀北地区年最大负荷进行建模预测,误差对比分析表明支持向量机的预测精度更高;从预测结果看,冀北地区年最大负荷波动较小,年均增长率为0.78%。预测结果可为冀北地区电力发展提供参考。

    Abstract:

    The electric power in northern Hebei Province, shoulders the special mission of ensuring the power supply security of the capital, serving the economic and social development of northern Hebei and serving the new energy development of the country. On the premise of analyzing the external environment that affects the load, this paper used support vector machine and BP neural network model to forecast the annual maximum load in northern Hebei. The error analysis showed that the forecast accuracy of support vector machine is higher; from the prediction results, the annual maximum load in northern Hebei fluctuates less with an average annual growth rate of 0.78%. The forecast result can provide a reference for the development of electric power in northern Hebei Province

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李顺昕,汲国强,康辉,等. 冀北地区年度最大负荷的支持向量机建模预测[J]. 科学技术与工程, 2019, 19(36): 179-183.
Li Shunxin, Ji Guoqiang, Kang Hui, et al. Research on Modeling Forecast of Annual Maximum Load in Northern Hebei Province Based on Support Vector Machine[J]. Science Technology and Engineering,2019,19(36):179-183.

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历史
  • 收稿日期:2019-06-27
  • 最后修改日期:2019-08-05
  • 录用日期:2019-11-22
  • 在线发布日期: 2020-01-21
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