应用模糊神经网络的短期负荷预测  

The Short term Load Forecasting by Applying the Fuzzy Neural Net

作　　者：王晓文[1] 梁志珊[2] 

机构地区：[1]沈阳电力高等专科学校 [2]东北电力学院

出　　处：《东北电力技术》1999年第2期1-3,共3页Northeast Electric Power Technology

摘　　要：提出用于短期负荷预测的模糊系统。该系统具有神经网络的结构和学习算法，称模糊神经网络ＦＮＮ。ＦＮＮ以现有的历史负荷生成规则，以最小隶属度法增补规则。规则参数经过修正后，ＦＮＮ的输出能与负荷数据很好地吻合。一经训练，ＦＮＮ就能预报未来负荷。The fuzzy system used for short-term load forecasting is put forward. This system, possesses the structure of neural net and learning algorithm, addressed as fuzzy neural net FNN. FNN generates the rules with the existing history loads and supplements the rules with minimun membership method. After the parameters of rule have been amended, the output of FNN can be well coincident with the data of loads. Once being trained, FNN can forecast future loads right away.

关 键 词：人工神经网络 模糊系统 负荷预测 电力系统 学习算法 

分 类 号：TM715[电气工程—电力系统及自动化] TP183[自动化与计算机技术—控制理论与控制工程]