期刊文献+

基于SA-LSSVM的电力短期负荷预测 被引量:5

Power Short-term Load Forecasting Based on SA-LSSVM
下载PDF
导出
摘要 提出融合模拟退火(Simulated annealing,SA)和最小二乘支持向量机(Least Square Support Vector Machine,LSSVM)的电力短期负荷预测方法。由于LSSVM的预测精度依赖于其参数的选择,并且难以选取合适的参数值,因此,参数选择是LSS-VM的一个关键问题。为了提高参数选择的质量和效率,采用SA算法进行LSSVM的参数寻优。以某市2010年1月1日至2011年1月7日的电力负荷数据和气象数据进行仿真实验,实验结果表明该方法具有较高的预测精度。 A power short-term load forecasting method using simulated annealing and least square support vec- tor machine is proposed. Because its prediction accuracy is dependent on the choice of its parameters, and it is very difficult to select the appropriate parameter values, therefore parameter selection is a key issue in LSSVM. In order to improve the quality and efficiency of parameter selection, the SA algorithm is used to optimize the parameters of LSSVM. The proposed model is applied to the short-term electrical power load forecasting using power load and meteorological data of a city in China from 2010--1--1 to 2011--1--7. The experimental results show that the pro- posed method has higher prediction accuracy.
作者 朱兴统
出处 《科学技术与工程》 北大核心 2012年第24期6171-6174,共4页 Science Technology and Engineering
关键词 最小二乘支持向量机 模拟退火 短期负荷预测 预测精度 least squares support vector machine simulated annealing short-term load forecasting prediction accuracy
  • 相关文献

参考文献10

  • 1牛东晓,曹树华,卢建昌,等.电力负荷预测技术及其应用(第2版).北京:中国电力出版社,2009.
  • 2师彪,李郁侠,于新花,闫旺.基于改进粒子群-模糊神经网络的短期电力负荷预测[J].系统工程理论与实践,2010,30(1):157-166. 被引量:46
  • 3Li Xiaocong, Wang Le, Li Qiuwen, et al. The short-term load forecasting based on grey gheory and RBF neural network. The 3rd Asia-Pacific Power and Energy Engineering Conference. Piscataway, N J, USA:IEEE,2011:1-4.
  • 4Wang Jingzhi. Method of short-term load forecasting based on BAYESIAN theorem. 2011 International Conference on Mechatronic Science, Electric Engineering and Computer. Piscataway, N J, USA: IEEE, 2011:966-969.
  • 5蒋喆.支持向量机在电力负荷预测中的应用研究[J].计算机仿真,2010,27(8):282-285. 被引量:21
  • 6曾勍炜,徐知海,吴键.基于粒子群优化和支持向量机的电力负荷预测[J].微电子学与计算机,2011,28(1):147-149. 被引量:30
  • 7Cortes C, Vapnik V N. Supporter vector networks. Machine Learning, 1995 ;20(3) :273-297.
  • 8Suykens J A K, Vandewalle J. Least squares support vector machine classifiers. Neural Processing Letters, 1999 ;9 ( 3 ) :293-300.
  • 9Kirkpatrick S, Gelatt C D ,Vecchi M P. Optimization by simulated annealing. Science, 1983 ;220(4598). :671-680.
  • 10Metropolis N,Rosenbluth A,Rosenbluth M, et al. Equation of state calculations by fast computing machines. Journal of Chemical Physics,1953 ;21 (6) :1087-1092.

二级参考文献18

共引文献94

同被引文献47

引证文献5

二级引证文献78

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部