Short-term Wind Power Ramp Forecasting with Empirical Mode Decomposition based Ensemble Learning Techniques

被引：0

作者：

Qiu, Xueheng ^{[1
]}

Ren, Ye ^{[1
]}

Suganthan, P. N. ^{[1
]}

Amaratunga, Gehan A. J. ^{[2
]}

机构：

[1] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore, Singapore

[2] Univ Cambridge, Dept Engn, Cambridge, England

来源：

2017 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI) | 2017年

基金：

新加坡国家研究基金会;

关键词：

Wind Power Forecasting; Power Ramp Classification; Power Ramp Rate; Ensemble Learning; Empirical Mode Decomposition; Random Vector Functional Link; Kernel Ridge; Regression; NEURAL-NETWORKS; ALGORITHM;

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：

Wind is a clean and renewable energy source with huge potential in power generation. However, due to the intermittent nature of the wind, the power generated by wind farms fluctuates and often has large ramps, which are harmful to the power grid. This paper presents algorithms to forecast the ramps in the wind power generation. The challenges of accurate wind power ramp forecasting are addressed. Wind power ramp and power ramp rate are defined. An ensemble method composed of empirical mode decomposition (EMD), kernel ridge regression (KRR) and random vector functional link (RVFL) network is employed to forecast the wind power ramp and the ramp rate. The performance of the proposed method is evaluated by comparing with several benchmark models based on both accuracy and efficiency. Possible future research directions are also identified.

引用

页码：1813 / 1820

页数：8

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