Wind speed forecasting by a hysteretic neural network based on Kalman filtering

被引：0
|
作者：
Li, Yan-Qing [1 ]
Xiu, Chun-Bo [2 ]
Zhang, Xin [2 ]
机构：
[1] School of Mathematics and Physics, University of Science and Technology Beijing, Beijing
[2] Tianjin Key Laboratory of Advanced Electrical Engineering and Energy Technology, Tianjin Polytechnic University, Tianjin
来源：
Beijing Keji Daxue Xuebao/Journal of University of Science and Technology Beijing | 2014年 / 36卷 / 08期
关键词：
Forecasting; Hysteresis; Kalman filtering; Neural networks; Wind energy power generation; Wind speed;
D O I：
10.13374/j.issn1001-053x.2014.08.018
中图分类号：
学科分类号：
摘要：
The hysteretic characteristic was introduced into the activation functions of neurons, and a forward hysteretic neural network was proposed. In combination with the Kalman filter algorithm, the hysteretic neural network was applied to wind speed forecasting. A change rate series of wind speed was constructed according to the original wind speed time series. Forecasting analysis of both the series was performed with the hysteretic neural network, these prediction results were fused using the Kalman filter algorithm, and thus the optimal estimated results were obtained. Simulation results show that the hysteretic neural network has more flexible structure, better generalization ability, and better prediction performance than the conventional neural network. The prediction performance can be further improved by Kalman filter fusion.
引用
收藏
页码：1108 / 1114
页数：6
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