Wind speed and wind power forecasting models

被引:1
|
作者
Lydia, M. [1 ]
Kumar, G. Edwin Prem [2 ]
Akash, R. [3 ]
机构
[1] Sri Krishna Coll Engn & Technol, Coimbatore 641008, India
[2] Sri Krishna Coll Engn & Technol, Dept Informat Technol, Coimbatore, India
[3] IIT Madras, Dept Elect Engn, Chennai, India
关键词
Deep learning; error metrics; forecasting; physical model; statistical model; time horizon; ARTIFICIAL NEURAL-NETWORKS; HYBRID MODEL; OPTIMIZATION ALGORITHM; PREDICTION; DECOMPOSITION; UNCERTAINTY; MULTISTEP; STRATEGY;
D O I
10.1177/0958305X241228515
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Sustainable energy resources have proved to be the best alternative in the wake of environmental degradation, depletion of ozone layer and ever-increasing demand for energy. Though wind energy is a promising resource, the non-linear nature and non-stationary characteristics of wind have remained a formidable challenge. Variability in wind power has posed numerous challenges in managing the power systems, especially in grid evacuation, penetration and integration. Forecasting wind is one of the powerful solutions to solve this problem. As the penetration of renewable energy sources is poised to increase in future, an accurate prediction can go a long way in helping the electricity grid to perform well. This article presents a review of existing research and recent trends in the forecasting of wind power and speed with a critical analysis of the contribution of every researcher. A review of forecasting technologies, data, time horizons, various forecasting approaches and error metrics has been presented in detail. The plethora of research issues that continue to challenge power system operators, wind farm owners and other stakeholders has been highlighted. The development of models for wind power or wind speed forecasting with excellent reliability and outstanding accuracy is the need of the hour.
引用
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页数:37
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