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.
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.
出处
《上海电力学院学报》
CAS
2014年第2期131-135,共5页
Journal of Shanghai University of Electric Power
基金
上海市教育委员会创新基金(11YZ192)
关键词
粒子群
BP神经网络
负荷预测
particle swarm
BP neural network
load forecasting
