电力系统及其自动化学报2017，Vol.29Issue(5)：35-40,6.DOI:10.3969/j.issn.1003-8930.2017.05.006
成分分解方法预测月度电力负荷
Monthly Load Forecasting Using Component Decomposition Method
摘要
Abstract
In this paper,a monthly load forecasting method based on X-12-ARIMA model with seasonal adjustment is proposed to improve the performance of load forecasting.Firstly,the effects of factors such as outliers,work days and leap years are eliminated from the original load data. Secondly,H-P filtering method is used to decompose the sequenc?es of trend and cycle obtained after seasonal adjustment. According to the characteristics of the component sequences of long term trend,cycle,seasonal factors and irregularity,an appropriate forecasting model is selected,and the final re?sult is obtained.Through an empirical test on the load data in Gansu area for 188 months,it is indicated that the pro?posed method is reliable and effective .关键词
离群值/月度负荷/季节调整/成分分解Key words
outlier/monthly load/seasonal adjustment/component decomposition分类
信息技术与安全科学引用本文复制引用
龙勇,苏振宇,盖晓平..成分分解方法预测月度电力负荷[J].电力系统及其自动化学报,2017,29(5):35-40,6.基金项目
国家社会科学基金重点资助项目(14AZD130) （14AZD130）
