Wind Power Forecasting using Emotional Neural Networks

被引:0
|
作者
Lotfi, Ehsan [1 ]
Khosravi, Abbas [2 ]
Akbarzadeh-T, M-R. [3 ,4 ]
Nahavandi, Saeid [5 ,6 ,7 ]
机构
[1] Islamic Azad Univ, Torbat E Jam Branch, Dept Comp Engn, Torbat E Jam, Iran
[2] Deakin Univ, Ctr Intelligence Syst Res, Geelong, Vic 3217, Australia
[3] Ferdowsi Univ Mashhad, Dept Elect Engn, Ctr Excellence Soft Comp & Intelligent Informat, Mashhad, Iran
[4] Ferdowsi Univ Mashhad, Dept Comp Engn, Ctr Excellence Soft Comp & Intelligent Informat, Mashhad, Iran
[5] Deakin Univ, Waurn Ponds, Vic 3216, Australia
[6] Deakin Univ, Engn, Waurn Ponds, Vic 3216, Australia
[7] Deakin Univ, Ctr Intelligent Syst Res, Waurn Ponds, Vic 3216, Australia
来源
2014 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC) | 2014年
关键词
emotion; BEL; BELBIC; forecasting; wind power; SHORT-TERM LOAD; PREDICTION; CONSTRUCTION; INTERVALS;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Emotional neural network (ENN) is a recently developed methodology that uses simulated emotions aiding its learning process. ENN is motivated by neurophysiological knowledge of the human's emotional brain. In this paper, ENNs are developed and examined for prediction tasks. Genetic algorithm is applied for optimal tuning of crisp numerical parameters of ENN. The performance of the proposed ENN is examined using data sets for a couple of synthetic (with constant and variable noise) and real world (wind farm power generation data) case studies. A traditional artificial neural network (ANN) is also implemented for comparison purposes. Numerical results indicate the superiority of ENN over ANN in terms of accuracy and stability.
引用
收藏
页码:311 / 316
页数:6
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