Short term wind power forecasting using Adaptive Neuro-Fuzzy Inference Systems

被引:0
|
作者
Johnson, Peter L. [1 ]
Negnevitsky, Michael [1 ]
Muttaqi, Kashern M. [1 ]
机构
[1] Univ Tasmania, Ctr Renewable Energy & Power Syst, Hobart, Tas, Australia
关键词
Adaptive Neuro-Fuzzy Inference System (ANFIS); short term forecasting; wind power;
D O I
暂无
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
As the global political will to address climate change gains momentum, the issues associated with integrating an increasing penetration of wind power into power systems need to be addressed. This paper summarises the current trends in wind power and how it is accepted into electricity markets. The need for accurate short term wind power forecasting is highlighted with particular reference to the five minute dispatch interval for the proposed Australian Wind Energy Forecasting System. Results from a case study show that Adaptive Neuro-Fuzzy Inference System (ANFIS) models can be a useful too] for short term wind power forecasting providing a performance improvement over the industry standard "persistence" approach.
引用
收藏
页码:487 / 492
页数:6
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