摘要
为了进一步提高农村电力系统短期负荷预测模型的性能,实现准确与快速预测农村电力系统负荷的目的,将蚁群算法(ACA)作为BP神经网络的学习算法,构造了一种蚁群神经网络(ACAN)预测模型。对某农村地区电力系统短期负荷预测的计算实例表明,基于蚁群神经网络的负荷预测方法与传统的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效果。
In order to improve capacity of rural short - term load forecasting of powersystem and make short term load forecasting more accurate and fast, a kind of Ant Colony Algorithm neural networks forecasting model is established by using the Ant Colony Algorithm to train the BP neural networks. The Ant Colony Algorithm is used to optimize the feed -forward neural networks in the design of this kind of neural networks. The results of an example of rural short term power system load forecasting show the better adaptive ability and forecasting effect of the CACN than those of the traditional BP neural network method.
出处
《农机化研究》
北大核心
2008年第10期176-179,184,共5页
Journal of Agricultural Mechanization Research
关键词
蚁群算法
神经网络
短期负荷预测
农村电网
ant colony algorithm
neural network
short -term load forecasting
rural power grid
