模糊规则控制的RBF短期负荷预测  

Short Term Load Forecasting Based on RBF by Fuzzy Rule Controled

作　　者：张德玲[1] 陈根永[1] 郭飞[1] 

机构地区：[1]郑州大学电气工程学院,郑州450001

出　　处：《微计算机信息》2007年第34期253-254,293,共3页Control & Automation

基　　金：河南省科技攻关项目(0424220182)

摘　　要：影响电网负荷预测的因素很多,针对地区电网负荷易受气象因素影响的特点,把气象因素作为影响负荷的主要因素,采用模糊规则控制的径向基神经网络(RBF)算法,对某地区电网的日负荷数据进行预测,与实际负荷比较可见根据各地区实际气象变化,采用这种预测方法可以提高负荷预测的速度和精度。Short-term load forecasting is affected by many factors. Taking account of the weather influence on different load sections, weather condition is looked as the main influential factor on load. In this paper, a load forecasting algorithm based on radial basis neural network by fuzzy rule controled is described. This method is practiced on the load forecasting of part of the power systera. Practical test on different areas shows that this method can improve the load forecasting speed and precision.

关 键 词：短期负荷预测 气象因素 神经网络 模糊控制理论 

分 类 号：TP273.3[自动化与计算机技术—检测技术与自动化装置]