Electricity Prices Forecasting using Artificial Neural Networks

被引：69
|
作者：
Alanis, Alma Y. [1 ]
机构：
[1] Univ Guadalajara, Ctr Univ Ciencias Exactas & Ingn, Dept Ciencias Computac, Guadalajara, Jalisco, Mexico
来源：
IEEE LATIN AMERICA TRANSACTIONS | 2018年 / 16卷 / 01期
关键词：
Time series forecasting; Artificial neural networks; Kalman filter training; Electricity price forecasting; Auto-regression;
D O I：
10.1109/TLA.2018.8291461
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents the results of the use of training algorithms for recurrent neural networks based on the extended Kalman filter and its use in electric energy price prediction, for both cases: one-step ahead and n-step ahead. In addition, it is included the stability proof using the well-known Lyapunov methodology, for the proposed artificial neural network trained with an algorithm based on the extended Kalman filter. Finally, the applicability of the proposed prediction scheme is shown by mean of the one-step ahead and n-step ahead prediction using data from the European power system.
引用
收藏
页码：105 / 111
页数：7
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