Very Short-Term Prediction of Wind Farm Power: An Advanced Hybrid Intelligent Approach

被引：0
|
作者：
Peri, Ramya M. [1 ]
Mandal, Paras [2 ]
Hague, Ashraf U. [3 ]
Tseng, Bill [1 ]
机构：
[1] Univ Texas El Paso, Dept IMSE, El Paso, TX 79968 USA
[2] Univ Texas El Paso, Dept Elect & Comp Engn, El Paso, TX 79968 USA
[3] Teshmont Consultants LP, Power Study Grp, Calgary, AB, Canada
来源：
2015 51ST IEEE INDUSTRY APPLICATIONS SOCIETY ANNUAL MEETING | 2015年
关键词：
Emotional brain; emotional neural networks; similar day method; very short-term wind power forecasting; wavelet transform;
D O I：
暂无
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：
This paper presents a new hybrid intelligent technique for very short-term wind power forecasting (VSWPF) based on the combination of wavelet transform (WT), similar day (SD) method, and emotional neural networks (ENN), i.e., WT+SD+ENN. The forecasting procedure using the proposed hybrid WT+SD+ENN intelligent model involves the refinement of the forecasted output obtained from the SD method by an application of ENN. The predicting performance of the proposed hybrid model is compared with the benchmark persistence method and other hybrid intelligent models in terms of mean absolute percentage error (MAPE), mean absolute error (MAE) and root mean square error (RMSE).
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页数：8
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