摘要
介绍了小波去噪的基本原理及方法,简要说明了其消噪的步骤。进一步说明了小波包去噪的原理和步骤,并给出了1个给定含噪信号去噪的实例。由于影响日负荷因素的复杂性,本文采用4个气象指数来量化温度、湿度和风速等气象因子对负荷的综合影响。首先介绍了小波去噪原理,然后通过小波去噪消除负荷和气象数据中的伪数据和噪声。结果表明,去噪以后从关联性上看效果显著,为最终进行负荷预测工作提供指导依据。
Due to the complexity of the factors that affecting daily load, four meteorological indices are applied to quantify the combined effects on daily load caused by temperature, humidity, wind speed and other meteorological factors. This paper describes the basic principles and methods of wavelet de-noising, and briefly explains the wavelet de-noising steps. To further demonstrate this technique, one practical example of wavelet de-noising with given noisy-containing signal is illustrated. The results show a significant correlation after the pseudo-data and noise from load and meteorological data is eliminated. This study provides a guidance for eventual load forecasting.
出处
《陕西电力》
2011年第12期14-17,共4页
Shanxi Electric Power
基金
国家自然科学基金资助项目(70901025)
关键词
日负荷
气象指数
小波去噪
伪数据
噪声
daily load
meteorological index
wavelet de-noising
pseudo-data
noise
