电力系统短期负荷的WGBR预测方法研究  

Wavelet Gradient Boosting Regression Method Study in Short-Term Load Forecasting

作　　者：王虹 谷根代[1] 

机构地区：[1]华北电力大学数理学院,河北保定

出　　处：《智能电网（汉斯）》2015年第4期189-196,共8页Smart Grid

基　　金：国家自然科学基金(11401208)项目资助。

摘　　要：考虑到气象因素对电力负荷的影响,并结合负荷和气象数据的特点,本文提出了基于小波变换的梯度Boosting回归(Gradient Boosting Regression based on Wavelet transform, WGBR)预测方法。该方法将负荷和气象数据分别分解为不同频段的小波子序列,对各子序列分别利用GBR算法建模预测,最后综合得到负荷序列预测结果。经华北地区某市的负荷数据验证得本文方法取得了较好的预测精度。The authors proposed gradient boosting regression method based on wavelet transform consi-dering the influence of weather factors and the characteristics of the load and meteorological data. The load and meteorological data were decomposed into several subsequences in different band by wavelet transform respectively. Forecasting the load subsequence by building different gradient boosting regression model, lastly, the final forecasting result is attained via adding all child-load-serials forecasting results. It has been showed by load data of a city in north China that the method achieved good prediction accuracy.

关 键 词：电力系统 短期负荷预测 小波变换 梯度Boosting回归 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]