全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
电网技术  2007 

基于累积式自回归动平均法和反向传播神经网络的短期负荷预测模型

, PP. 73-76

Keywords: 短期负荷预测,累积式自回归动平均法(ARIMA),BP神经网络,平滑性处理

Full-Text   Cite this paper   Add to My Lib

Abstract:

针对电力系统短期负荷的特点建立了将累积式自回归动平均法(autoregressiveintegratedmovingaverage,ARIMA)和采用反向传播算法(backpropagation,BP)的神经网络法相结合的短期负荷预测模型。该模型利用ARIMA方法对线性时间序列逼近能力强的特点首先对预测日负荷进行预测,然后应用BP神经网络方法对预测结果进行修正,因此克服了单一算法存在的不足。应用该模型对某地区电网进行负荷预测,结果表明该方法的预测效果较好

References

[1]  刘晨辉.电力系统负荷预报理论与方法[M].哈尔滨:哈尔滨工业大学出版社,1987.
[2]  张民,鲍海,晏玲,等.基于卡尔曼滤波的短期负荷预测方法的研究[J].电网技术,2003,27(10):39-42.
[3]  Zhang Min,Bao Hai,Yan Ling,et al.Research on processing of short-term historical data of daily load based on Kalman filter [J].Power System Technology,2003,27(10):39-42(in Chinese).
[4]    徐进东,丁晓群,邓勇.基于相似日的线性外推短期负荷预测[J].继电器,2005,33(7):37-39.
[5]  Xu Jindong,Ding Xiaoqun,Deng Yong.Short-term load forecast using linear extrapolation based on similar historical day data [J].Relay,2005,33(7):37-39(in Chinese).
[6]  周佃民,管晓宏,孙捷,等.基于神经网络的电力系统短期负荷预测研究[J].电网技术,2002,26(2):10-13.
[7]  Zhou Dianmin,Guan Xiaohong,Sun Jie,et al.A short-term load forecasting system based on BP artificial neural network [J].Power System Technology,2002,26(2):10-13(in Chinese).
[8]  赵登福,张涛,杨增辉,等.基于GN-BFGS算法的RBF神经网络短期负荷预测[J].电力系统自动化,2003,27(4):33-36.
[9]  Zhao Dengfu,Zhang Tao,Yang Zenghui,et al.Short-term load forecasting using radial basis function (RBF) neural networks based on GN-BFGS algorithm[J].Automation of Electric Power Systems,2003,27(4):33-36(in Chinese).
[10]  谢开贵,李春燕,周家启.基于神经网络的负荷组合预测模型研究[J].中国电机工程学报,2002,22(7):85-89.
[11]  Xie Kaigui,Li Chunyan,Zhou Jiaqi.Research of the combination forecasting model for load based on artificial neural network [J].Proceedings of the CSEE,2002,22(7):85-89(in Chinese).
[12]  金海峰,熊信艮,吴耀武.基于级联神经网络的短期负荷预测方法[J].电网技术,2002,26(3):4951.
[13]  Jin Haifeng,Xiong Xinyin,Wu Yaowu.A short-term load forecasting method based on cascade neural network[J].Power System Technology,2002,26(3):49-51(in Chinese).
[14]  马文晓,白晓民,沐连顺.基于人工神经网络和模糊推理的短期负荷预测方法[J].电网技术,2003,27(5):29-32.
[15]  Ma Wenxiao,Bai Xiaomin,Mu Lianshun.Short term load forecasting using artificial neuron network and fuzzy inference [J].Power System Technology,2003,27(5):29-32(in Chinese).
[16]  张涛,赵登福,周琳,等.基于RBF神经网络和专家系统的短期负荷预测方法[J].西安交通大学学报,2001,35(4):331-334.
[17]  Zhang Tao,Zhao Dengfu,Zhou Lin,et al.Short-term load forecasting using radial basis function networks and expert system [J].Journal of Xi'an Jiaotong University,2001,35(4):331-334(in Chinese).
[18]  赵登福,王蒙,张讲社,等.基于支持向量机方法的短期负荷预测[J].中国电机工程学报,2002,22(4):26-30.
[19]  Zhao Dengfu,Wang Meng,Zhang Jiangshe,et al.A support vector machine approach for short term load forecasting[J].Proceedings of the CSEE,2002,22(4):26-30(in Chinese).
[20]  张林,刘先珊,阴和俊.基于时间序列的支持向量机在负荷预测中的应用[J].电网技术,2004,28(19):38-41.
[21]  Zhang Lin,Liu Xianshan,Yin Hejun.Application of support vector machines based on time sequence in power system load forecasting [J].Power System Technology,2004,28(19):38-41(in Chinese).
[22]  姜勇.基于模糊聚类的神经网络短期负荷预测方法[J].电网技术,2003,27(2):45-49.
[23]  Jiang Yong.Short-term load forecasting using a neural network based on fuzzy clustering [J].Power System Technology,2003,27(2):45-49(in Chinese).
[24]  刘小华,刘沛,张步涵,等.逐级均值聚类算法的RBFN模型在负荷预测中的应用[J].中国电机工程学报,2004,24(2):17-21.
[25]  Liu Xiaohua,Liu Pei,Zhang Buhan,et al.Application of RBFN model for load forecasting based on ranking means clustering [J].Proceedings of the CSEE,2004,24(2):17-21(in Chinese).
[26]  赖晓平,周鸿兴,田发中.电力系统短期负荷预测的混合模型神经元网络方法[J].电网技术,2000,24(1):47-51.
[27]  Lai Xiaoping,Zhou Hongxing,Tian Fazhong.A hybrid model neural network based approach to short-term load forecasting[J].Power System Technology,2000,24(1):47-51(in Chinese).
[28]  周明,严正,倪以信,等.含误差预测校正的ARIMA电价预测新方法[J].中国电机工程学报,2004,24(12):63-68.
[29]  Zhou Ming,Yan Zheng,Ni Yixin,et al.A novel ARIMA approach on electricity price forecasting with the improvement of predicted error[J].Proceedings of the CSEE,2004,24(12):63-68(in Chinese).

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133