[1]朱震曙,薄煜明,吴盘龙,等.基于QPSO-RBFNN的短期电力负荷预测模型[J].南京理工大学学报(自然科学版),2016,40(01):97.
 Zhu Zhenshu,Bo Yuming,Wu Panlong,et al.Short-term power load forecasting model based on QPSO-RBFNN[J].Journal of Nanjing University of Science and Technology,2016,40(01):97.
点击复制

基于QPSO-RBFNN的短期电力负荷预测模型
分享到:

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
40卷
期数:
2016年01期
页码:
97
栏目:
出版日期:
2016-02-29

文章信息/Info

Title:
Short-term power load forecasting model based on QPSO-RBFNN
作者:
朱震曙薄煜明吴盘龙赵高鹏朱建良
南京理工大学 自动化学院,江苏 南京 210094
Author(s):
Zhu ZhenshuBo YumingWu PanlongZhao GaopengZhu Jianliang
School of Automation,Nanjing University of Science and Technology,Nanjing 210094,China
关键词:
量子行为粒子群优化 电力负荷 负荷预测 径向基函数 神经网络 K-均值聚类 权值 均方根误差
Keywords:
quantum-behaved particle swarm optimization power load load forecasting radical basis function neural network K-means clustering weights root mean square error
分类号:
TM715
摘要:
为了提高短期电力负荷预测的精度,提出了一种量子行为粒子群优化(Quantum-behaved particle swarm optimization,QPSO)算法和径向基函数神经网络(Radical basis function neural network,RBFNN)相结合的电力负荷短期预测模型。通过K-均值聚类算法确定RBFNN的基函数中心,并用粒子群优化算法优化神经网络权值,在加快RBFNN收敛速度的同时提高预测精度。以实际负荷数据进行预测验证,预测负荷的均方根误差小于0.01,验证了模型的合理性和有效性。
Abstract:
In order to improve the accuracy of short-term power load forecasting,a forecasting model is proposed by combining quantum-behaved particle swarm optimization(QPSO)algorithm with radical basis function neural network(RBFNN).The basis function center of the RBFNN is obtained by the K-means clustering algorithm.The neural network weights are optimized by the particle swarm optimization algorithm.The convergence rate of the RBFNN is fastened while the forecasting accuracy is raised.The short-term power load forecasting is verified by real load data,the root mean square error of daily load forecasting is less than 0.01,and the rationality and validity of the model are demonstrated.

参考文献/References:

[1] 牛东晓,曹树华.电力负荷预测技术及其应用[M].北京:中国电力出版社,1998.
[2]李光珍,刘文颖.基于LSSVM和马尔可夫链的母线负荷短期预测[J].电力系统保护与控制,2010,38(11):55-59,66.

Li Guangzhen,Liu Wenying.Bus load short-term forecast based on LSSVM and Markov chain[J].Power System Protection and Control,2010,38(11):55-59,66.
[3]L Hock-Eam,Y Chee-Yin.Forecasting electricity usage using univariate time series models[J].AIP Conference Proceedings,2014,1635(1):799-804.
[4]杨国健,杨镜非,童开蒙,等.短期负荷预测的支持向量机参数选择方法[J].电力系统及其自动化学报,2012,24(6):148-151.
Yang Guojian,Yang Jingfei,Tong Kaimeng,et al.Parameter selection of support vector machine for short-term load forecasting[J].Proceedings of the CSU-EPSA,2012,24(6):148-151.
[5]张冬青,宁宣熙,刘雪妮.基于RBF神经网络的非线性时间序列在线预测[J].控制理论与应用,2009,26(2):151-155.
Zhang Dongqing,Ning Xuanxi,Liu Xueni.On-line prediction of nonlinear time series using RBF neural networks[J].Control Theory & Applications,2009,26(2):151-155.
[6]何耀耀,许启发,杨善林,等.基于RBF神经网络分位数回归的电力负荷概率密度预测方法[J].中国电机工程学报,2013,33(1):93-98.
He Yaoyao,Xu Qifa,Yang Shanlin,et al.A power load probability density forecasting method based on RBF neural network quantile regression[J].Proceedings of the CSEE,2013,33(1):93-98.
[7]Li Cunhe,Shi Guoqiang.Weights optimization for multi-instance multi-label RBF neural networks using steepest descent method[J].Neural Computing and Applications,2013,22(7-8):1563-1569.
[8]姜鸿羽,马宏忠,梁欢,等.改进粒子BP神经网络在变电站噪声控制中的应用[J].中国电力,2014,47(9):71-76.
Jiang Hongyu,Ma Hongzhong,Liang Huan,et al.The application of improved particle BP neural network for substation noise control[J].Electric Power,2014,47(9):71-76.
[9]Langeveld J,Engelbrecht A P.Set-based particle swarm optimization applied to the multidimensional knapsack problem[J].Swarm Intelligence,2012,6(4):297-342.
[10]杨廷志,文小飞,万俊,等.改进神经网络的短期负荷预测模型及仿真[J].计算机仿真,2014,31(10):145-150,176.
Yang Tingzhi,Wen Xiaofei,Wan Jun,et al.Short-term power load forecasting model and simulation based on neural network[J].Computer Simulation,2014,31(10):145-150,176.
[11]姜林.基于粒子群优化神经网络的电力短期负荷预测研究[D].阜阳:辽宁工程技术大学电器与控制工程学院,2011.
[12]Kennedy J,Eberhart R C.Particle swarm optimization[C]//Proceedings of IEEE International Conference on Neural Networks.Piscataway,NJ:IEEE,1995:1942-1948.
[13]孙俊.量子行为粒子群优化算法研究[D].无锡:江南大学信息工程学院,2009.

相似文献/References:

[1]施展 ,陈庆伟.基于改进的多目标量子行为粒子群优化算法的多无人机协同任务分配[J].南京理工大学学报(自然科学版),2012,36(06):0.
 SHI Zhan,CHEN Qing wei.Cooperative Task Allocation for Multiple UAVs Based on Improved Multiobjective Quantumbehaved Particle Swarm Optimization Algorithm[J].Journal of Nanjing University of Science and Technology,2012,36(01):0.
[2]孙新程,孔建寿,刘 钊.基于核主成分分析与改进神经网络的电力负荷中期预测模型[J].南京理工大学学报(自然科学版),2018,42(03):259.[doi:10.14177/j.cnki.32-1397n.2018.42.03.001]
 Sun Xincheng,Kong Jianshou,Liu Zhao.Middle-term power load forecasting model based on kernel principalcomponent analysis and improved neural network[J].Journal of Nanjing University of Science and Technology,2018,42(01):259.[doi:10.14177/j.cnki.32-1397n.2018.42.03.001]

备注/Memo

备注/Memo:
收稿日期:2015-04-24 修回日期:2015-08-05
基金项目:国家自然科学基金(61473153; 61203266); 江苏省产学研联合创新资金(BY2013004-04)
作者简介:朱震曙(1987-),男,博士生,主要研究方向:电力系统智能控制,E-mail:zhuzzs0411@126.com; 通讯作者:薄煜明(1965-),男,研究员,主要研究方向:导航制导与控制,E-mail:byming@njust.edu.cn。
引文格式:朱震曙,薄煜明,吴盘龙,等.基于QPSO-RBFNN的短期电力负荷预测模型[J].南京理工大学学报,2016,40(1):97-101.
投稿网址:http://zrxuebao.njust.edu.cn
DOI:10.14177/j.cnki.32-1397n.2016.40.01.016
更新日期/Last Update: 2016-02-29