Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Electricity Prices Forecasting

被引：200

作者：

Catalao, J. P. S. ^{[1
,2
]}

Pousinho, H. M. I. ^{[1
,2
]}

Mendes, V. M. F. ^{[3
]}

机构：

[1] Univ Beira Interior, Covilha, Portugal

[2] IST, Ctr Innovat Elect & Energy Engn, Lisbon, Portugal

[3] Inst Super Engn Lisboa, Lisbon, Portugal

来源：

IEEE TRANSACTIONS ON POWER SYSTEMS | 2011年 / 26卷 / 01期

关键词：

Electricity market; fuzzy logic; neural networks; price forecasting; swarm optimization; wavelet transform; NEURAL-NETWORK; MARKETS; DECOMPOSITION; INFORMATION; TRANSFORM; MODELS;

D O I：

10.1109/TPWRS.2010.2049385

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

080906 [电磁信息功能材料与结构]; 082806 [农业信息与电气工程];

摘要：

A novel hybrid approach, combining wavelet transform, particle swarm optimization, and adaptive-network-based fuzzy inference system, is proposed in this paper for short-term electricity prices forecasting in a competitive market. Results from a case study based on the electricity market of mainland Spain are presented. A thorough comparison is carried out, taking into account the results of previous publications. Finally, conclusions are duly drawn.

引用

页码：137 / 144

页数：8

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