摘要
针对电力系统短期负荷的变化与影响因素间的复杂非线性关系,首先,提出用BP神经网络进行负荷预测,接着,在输入变量的选择上引入了负荷日期和气象温度,对于日期变量分为工作日和休息日,对于气温变量进行分段处理。最后通过实例仿真表明该方法可以取得较高的预测精度。
For the complex non-linear relation of short-term load forecasting of electric power system and influencing factors ,first, BP neural network for the short-term load forecast is proposed,then,load dates and meteorological temperature are added to the input variables of BP neural network, the date variables are classified into working day and rest day, temperature variables are treated with the segmentation. At last, the calculation result of study indicates that this method can gain a higher forecasting precision.
出处
《电气开关》
2009年第4期34-37,共4页
Electric Switchgear
关键词
短期负荷预测
BP神经网络
负荷日期
气象温度
short-term load forecasting
BP neural network
load date
meteorological temperature
