| 基于改进粒子群算法的电力系统负荷预测 |
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| 引用本文： | 刘伟,梁新兰,姚洁.基于改进粒子群算法的电力系统负荷预测[J].科学技术与工程,2009,9(15). |
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| 作者姓名： | 刘伟 梁新兰 姚洁 |
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| 作者单位： | 大庆石油学院,电气信息工程学院,大庆,163318 |
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| 基金项目： | 黑龙江自然科学基金项目 |
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| 摘 要： | 为了提高电力系统负荷预测的精度,并考虑到电力系统负荷的混沌特性,提出了将蜜蜂进化型粒子群算法和混沌神经网络相结合的负荷预测方法.构建了混沌神经网络模型,提出了蜜蜂进化型PSO算法(Bee Evolution Modifying Particle Swarm Optimization, BEMPSO);以此来训练混沌神经网络参数,并且分别对基本粒子群优化算法和BEMPSO优化算法的模型进行仿真预测.结果表明提出的BEMPSO混沌神经网络负荷预测方法具有较强的泛化能力和较高的精度.
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| 关 键 词： | 电力系统 负荷预测 蜜蜂进化 粒子群算法 混沌神经网络 |
Power System Load Forecasting Based on BEMPSO Chaotic Neural Network |
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| LIU Wei,LIANG Xin-lan,YAO Jie.Power System Load Forecasting Based on BEMPSO Chaotic Neural Network[J].Science Technology and Engineering,2009,9(15). |
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| Authors: | LIU Wei LIANG Xin-lan YAO Jie |
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| Institution: | School of Electric Information Engineering of Daqing Petroleum Institute;Daqing 163318;P.R.China |
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| Abstract: | Considering the chaotic characteristic of power system load,a method based on bee evolution modifying particle swarm optimization (BEMPSO) and chaotic neural network are presented for power system load forecasting to improve precision. The chaotic neural networks model and integrates bee evolution modifying with particle swarm optimization are build. The novel BEMPSO algorithm is proposed. It is used to train connection weights of multi-layer feed forward neural network until the learning error tends to be ... |
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| Keywords: | power system load forecasting bee evolution particle swarm optimization chaotic neural networks |
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