马建伟,张国立

• 1:华北电力大学
• 2:华北电力大学 河北省保定市071003
• 3:河北省保定市071003

摘要(Abstract)：

短期负荷预测结果对电力系统的经济效益具有重要影响。人工鱼群算法是最新提出的新型寻优策略,具有良好的克服局部极值、获得全局极值的能力。文章建立了一种新的人工鱼群神经网络预测模型,利用人工鱼群算法训练神经网络的权值,再将该神经网络用于短期负荷预测。对某电力系统进行的负荷预测结果表明,该方法与传统的BP神经网络预测方法相比具有较强的自适应能力和较好的预测效果。

关键词(KeyWords)： 人工鱼群算法;神经网络;短期负荷预测;电力系统

Abstract:

Keywords:

基金项目(Foundation):

作者(Author): 马建伟,张国立

Email:

DOI:

参考文献(References)：

• [1]刘晨晖.电力系统负荷预报理论和方法[M].哈尔滨:哈尔滨工业大学出版社,1987.
• [2]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.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.
• [3]尤勇,盛万兴,王孙安.基于人工免疫网络的短期负荷预测模型[J].中国电机工程学报,2003,23(3):26-30.You Yong,Sheng Wanxing,Wang Sun’an.Short-term load forecasting using artificial immune network[J].Proceedings of the CSEE,2003,23(3):26-30.
• [4]周佃民,管晓宏,孙婕,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):10-14.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):10-14.
• [5]郑岗,刘斌,周勇,等.基于神经元网络的短期电力负荷预测[J].西安理工大学学报,2002,18(2):126-130.Zheng Gang,Liu Bin,Zhou Yong et al.Short-term load forecasting based on neural network[J].Journal of Xi’an University of Technology,2002,18(2):126-130.
• [6]邰能灵,侯志俭.小波模糊神经网络在电力系统短期负荷预测中的应用[J].中国电机工程学报,2004,24(1):24-29.Tai Nengling,Hou Zhijian.New short-term load forecasting principle with the wavelet transform fuzzy neural network for the power systems[J].Proceedings of the CSEE,2004,24(1):24-29.
• [7]尤勇,盛万兴,王孙安.一种新型短期负荷预测模型的研究及应用[J].中国电机工程学报,2002,22(9):15-18.You Yong,Sheng Wanxing,Wang Sun’an.The study and application of the electric power system short-term load forecasting using a new model[J].Proceedings of the CSEE,2002,22(9):15-18.
• [8]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.Jiang Yong.Short-term load forecasting using a neural network based on fuzzy clustering[J].Power System Technology,2003,27(2):45-49.
• [9]姚李孝,姚金雄,李宝庆,等.基于竞争分类的神经网络短期电力负荷预测[J].电网技术,2004,28(10):45-48.Yao Lixiao,Yao Jinxiong,Li Baoqing et al.Short-term load forecasting using neural network based on competitive learning classification[J].Power System Technology,2004,28(10):45-48.
• [10]Mcclelland J L,Rumelhart D E.Parallel distributed processing[M].Cambridge:MIT Press,1986.
• [11]Larsen E V,Miller N W,Nilsson S L et al.Benefits of GTO-based compensation systems for electric utility applications[J].IEEE Trans on Power Delivery,1992,7(4):2056-2061.
• [12]李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式:鱼群算法[J].系统工程理论与实践,2002,(11):32-38.
• [13]李晓磊,钱积新.基于分解协调的人工鱼群优化算法研究[J].电路与系统学报,2003,8(1):1-6.
• [14]岑文辉,雷友坤,谢恒.应用人工神经网络与遗传算法进行短期负荷预测[J].电力系统自动化,1997,21(3):29-32.Cen Wenhui,Lei Youkun,Xie Heng.The application of the electric power system short-term load forecasting using artificial network and genetic algorithm[J].Automation of Electric Power Systems,1997,21(3):29-32.
• [15]丁军威,孙雅明.基于混沌学习算法的神经网络短期负荷预测[J].电力系统自动化,2000,24(2):32-35.Ding Junwei,Sun Yaming.Short-term load forecasting using a neural network based on chaos learning algorithm[J].Automation of Electric Power Systems,2000,24(2):32-35.
• [16]马建伟,张国立,谢宏,等.利用人工鱼群算法优化前向神经网络[J].计算机应用,2004,24(10):21-23.Ma Jianwei,Zhang Guoli,Xie Hong et al.The optimization of feed-forward neural networks based on artificial fish-swarm algorithm[J].Computer Applications,2004,24(10):21-23.