Comparing Variational and Empirical Mode Decomposition in Forecasting Day-Ahead Energy Prices

被引：131

作者：

Lahmiri, Salim ^{[1
]}

机构：

[1] ESCA Sch Management, Casablanca 200670, Morocco

来源：

IEEE SYSTEMS JOURNAL | 2017年 / 11卷 / 03期

关键词：

Empirical mode decomposition (EMD); energy price; ensemble system; forecasting; generalized regression neural network; variational mode decomposition (VMD); ELECTRICITY PRICES; MARKETS;

D O I：

10.1109/JSYST.2015.2487339

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Recently, variational mode decomposition (VMD) has been proposed as an advanced multiresolution technique for signal processing. This study presents a VMD-based generalized regression neural network ensemble learning model to predict California electricity and Brent crude oil prices. Its performance is compared to that of the empirical mode decomposition (EMD) based generalized regression neural network (GRNN) ensemble model. Particle swarm optimization is used to optimize each GRNN initial weight within the ensemble system. Experimental results showed that the VMD-based ensemble outperformed EMD-based ensemble forecasting system in terms of mean absolute error, mean absolute percentage error, and root mean-squared error. It also outperformed the conventional auto-regressive moving average model used for comparison purpose. As a result, the VMD-based GRNN ensemble forecasting paradigm could be a promising methodology for California electricity and Brent crude oil price prediction.

引用

页码：1907 / 1910

页数：4

共 29 条

[1] Day-Ahead Price Forecasting of Electricity Markets by Mutual Information Technique and Cascaded Neuro-Evolutionary Algorithm [J].