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投稿时间：2013-04-10
投稿时间：2013-04-10
中文摘要: 电力负荷预测通常采用神经网络方法,该方法训练时间较长,并且由于负荷受到气象因素影响,该算法预测的精度不是很高.为了克服当前存在的问题,采用粒子群算法优化BP神经网络的权值和阈值,归一化处理气象因素,利用神经网络预测短期电力负荷.实验结果表明,该方法比单纯BP神经网络预测具有明显优势.
Abstract:Power load forecasting commonly uses neural network method.The training time of the method is longer,and the meteorological factors can affect load,so the prediction accuracy of the algorithm is not very high.In order to overcome the current problems,particle swarm algorithm is used for optimizing the weight and threshold of BP neural network,then meteorological factors are processed by normalization method and forecasting short-term power load through neural network.The experimental results show that this method has obvious advantages over mere BP neural network.
keywords: particle swarm BP neural network load forecasting
文章编号：20140208 中图分类号： 文献标志码：
基金项目:上海市教育委员会创新基金(11YZ192).
| 作者 | 单位 | |
| 陈帅 | 上海电力学院电气工程学院 | chenshuai1988@126.com |
| 王勇 | 上海电力学院电气工程学院 | |
| 吕丰 | 金山供电公司电力调度控制中心 | |
| 杨恒 | 上海电力学院电气工程学院 | |
| 黄亮亮 | 上海电力学院电气工程学院 |
引用文本：
陈帅,王勇,吕丰,等.基于粒子群BP神经网络的短期电力负荷预测[J].上海电力大学学报,2014,30(2):131-135.
CHEN Shuai,WANG Yong,LÜ Feng,et al.Forecasting Based on the Particle Swarm BP Neural Network[J].Journal of Shanghai University of Electric Power,2014,30(2):131-135.
陈帅,王勇,吕丰,等.基于粒子群BP神经网络的短期电力负荷预测[J].上海电力大学学报,2014,30(2):131-135.
CHEN Shuai,WANG Yong,LÜ Feng,et al.Forecasting Based on the Particle Swarm BP Neural Network[J].Journal of Shanghai University of Electric Power,2014,30(2):131-135.