Wind Power Short-Term Prediction by a Hybrid PSO-ANFIS Approach

被引：5
|
作者：
Pousinho, H. M. I. [1 ]
Catalao, J. P. S. [1 ]
Mendes, V. M. F. [2 ]
机构：
[1] Univ Beira Interior, Dept Electromech Engn, Covilha, Portugal
[2] Inst Super Engn Lisbon, Dept Elect Engn & Automat, Lisbon, Portugal
来源：
MELECON 2010: THE 15TH IEEE MEDITERRANEAN ELECTROTECHNICAL CONFERENCE | 2010年
关键词：
NEURAL-NETWORK; SYSTEM; SPEED;
D O I：
10.1109/MELCON.2010.5475923
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Wind power prediction plays a key role in tackling these challenges. A novel hybrid approach, combining particle swarm optimization and adaptive-network-based fuzzy inference system, is proposed in this paper for short-term wind power prediction. Results from a real-world case study are presented. Conclusions are duly drawn.
引用
收藏
页码：955 / 960
页数：6
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