电力系统保护与控制2009，Vol.37Issue(14)：59-62,85,5.
小波软阈值去噪和GRNN网络在月度负荷预测中的应用
Application of wavelet soft-threshold de-noising and GRNN in monthly load forecasting
刘学琴 1吴耀华 2崔宝华1
作者信息
- 1. 保定电力职业技术学院电气工程系,河北,保定,071051
- 2. 陕西理工学院电气工程系,陕西,汉中,723003
- 折叠
摘要
Abstract
Based on the de-noising theory of wavelet soft-threshold and the general regression neural network (GRNN), according to the property of increase trend and seasonal fluctuation of monthly history load, the paper proposes a new monthly load forecasting model. First, it de-noises data of load using wavelet soft-threshold and uses the horizontal and vertical de-noised history data as input of GRNN building the monthly load forecasting model. Then the new model is applied to forecast monthly load of somewhere. The forecasting result shows that it has good robustness and practicality as well as high precision.关键词
月度负荷预测/广义回归神经网络/小波软阈值/去噪Key words
monthly load forecasting/general regression neural network (GRNN)/wavelet soft-threshold/de-noising分类
信息技术与安全科学引用本文复制引用
刘学琴,吴耀华,崔宝华..小波软阈值去噪和GRNN网络在月度负荷预测中的应用[J].电力系统保护与控制,2009,37(14):59-62,85,5.
