摘要
国网冀北电力有限公司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。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。
The electric power in northern Hebei Province plays an important role to searve the ecnomic and social development of northern Hebei and the new energy development of the country.On the premise of analyzing the external environment that affects the load,support vector machine(SVM)and back propagation(BP)neural network model were used to forecast the annual maximum load in northern Hebei.The error analysis show 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.
作者
李顺昕
汲国强
康辉
丁健民
秦砺寒
厉艳
LI Shun-xin;JI Guo-qiang;KANG Hui;DING Jian-min;QIN Li-han;LI Yan(Research Institute of Economics and Technology,State Grid Jibei Electric Power Company,Beijing 100038,China;School of Economic and Management,North China Electric Power University,Beijing 102206,China)
出处
《科学技术与工程》
北大核心
2019年第36期179-183,共5页
Science Technology and Engineering
基金
国家自然科学基金(71471059)
高等学校学科创新引智计划(B18021)资助
关键词
冀北地区
环境分析
年最大负荷
支持向量机
建模预测
northern Hebei Province
external environment analysis
annual maximum load
support vector machine
modeling forecast
