Short-Term Load Forecasting Using Kalman Filter and Elman Neural Network

摘要：

In view of the dynamic nonlinear characteristics of power system loads, a short-term load forecasting (STLF) method for power system is proposed based on Wiener model, and Elman recursive neural network is used to fit in with its nonlinear part in this paper. Kalman filter is introduced to overcome the unknown disturbance in the linear part of the systems during loads prediction. Then, Elman neural network is applied to carry out the nonlinear loads prediction. The studies indicate that the proposed method possesses high learning efficiency, strong adaptability and high forecast precision, and is very suitable to short-term load forecast. At last, a simulation example indicates the availability of the method.

作者: Feng ZHAO Hongsheng SU

作者单位: Lanzhou Jiaotong University, China

会议类型: 国际会议

会议名称: 2nd IEEE Conference on Industrial Electronics and Applications(ICIEA 2007)(第二届IEEE工业电子与应用国际会议)

会议地点: 哈尔滨

会议语种:英文

在线出版日期: 2007-05-23（万方平台首次上网日期，不代表论文的发表时间）