AN OVERVIEW ON WIND POWER FORECASTING METHODS

被引:0
|
作者
Chai, Songjian [1 ]
Xu, Zhao [1 ]
Lai, Loi Lei [2 ]
Wong, Kit Po [3 ]
机构
[1] Hong Kong Polytech Univ, Dept Elect Engn, Hong Kong, Hong Kong, Peoples R China
[2] State Grid Energy Res Inst, Beijing, Peoples R China
[3] Univ Western Australia, Sch Elect Elect & Comp Engn, Perth, WA 6009, Australia
来源
PROCEEDINGS OF 2015 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOL. 2 | 2015年
关键词
Wind power forecast; Point forecast; Interval forecast; Probabilistic forecast; EXTREME LEARNING-MACHINE; PREDICTION INTERVALS; SPEED; WAVELET;
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
With the continually increasing growth in wind generation being integrated into the electric networks, it brings about significant challenges for decision-makers of power system operation due to its high volatility and uncertainty. One efficient approach to tackling such a problem is using tenable forecasting tools. As the conventional point forecasting can only provide a deterministic predicted value, instead, the probabilistic interval forecasting was attracted broad attention in the last few years since it can reflect the information of the uncertainties associated with wind power generation, which can significantly facilitate a large number of decision-making problems in power system operation. This paper presents an overview of current methods used in wind power forecasting. First of all, the frequently-used traditional point forecasting methods are reviewed. Afterwards, various state-of-the-art techniques in terms of probabilistic forecasting are discussed. The indications for future development in wind power forecasting approaches and conclusions are given in the end.
引用
收藏
页码:765 / 770
页数:6
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