摘要
在智能电网条件下,用户的用电模式将会发生重大变化,其中一个显著的变化就是用户可以根据电能需求结合实时电价调整其消费模式。这使得用户负荷预测更为复杂。在对影响短期电力负荷特性的各种因素进行分析的基础上,综合考虑了实时电价的影响,提出了一种用遗传算法优化改进的灰色神经网络方法,利用灰色模型可以弱化数据的随机性以及神经网络的高度非线性,对短期负荷进行预测,采用遗传算法对网络进行优化,从而提高了预测的精确度。实例证明该算法能较好地解决实时电价下的短期负荷预测问题。
In the condition of smart grid, there will be many great changes in power consumption pattern of power consumers, and a significant change in the power consumption pattern is that based on the real-time price consumers can adjust their electricity consumption modes and it makes the forecasting of power load complicated. On the basis of analyzing various factors impacting on short-term load characteristics, based on the synthetical consideration of real-time price a gray neural network method improved by genetic algorithm is proposed. Utilizing the property of gray model that the randomness of data can be reduced by it and the strong nonlinearity of neural network the short-term load forecasting is performed and the genetic algorithm is used to optimize the neural network to improve the accuracy of forecasting. Results of computation example show that the proposed algorithm is available to short-term load forecasting under real-time price.
出处
《电网技术》
EI
CSCD
北大核心
2012年第1期224-229,共6页
Power System Technology
基金
国家重点基础研究发展计划项目(973项目)(2009CB219700
2010CB234600)
国家863高技术基金项目(2007AA05Z250)
国家自然科学基金项目(50595412
50625722)~~
关键词
智能电网
实时电价
负荷预测
遗传算法
灰色神经网络
smart grid
real-time price
load forecasting
genetic algorithm
gray neural network