A review on multi-objective optimization framework in wind energy forecasting techniques and applications

被引:113
|
作者
Liu, Hui [1 ]
Li, Ye [1 ]
Duan, Zhu [1 ]
Chen, Chao [1 ]
机构
[1] Cent South Univ, Sch Traff & Transportat Engn, Inst Artificial Intelligence & Robot IAIR, Key Lab Traff Safety Track Minist Educ, Changsha 410075, Hunan, Peoples R China
基金
中国国家自然科学基金;
关键词
Wind energy forecasting; Multi-objective optimization; Pareto optimality; Objective functions; Wind energy applications; PARTICLE SWARM OPTIMIZATION; EXTREME LEARNING-MACHINE; FUZZY TIME-SERIES; PREDICTION INTERVALS; HYBRID MODEL; OBJECTIVE OPTIMIZATION; QUANTILE REGRESSION; FEATURE-SELECTION; NSGA-II; SPEED;
D O I
10.1016/j.enconman.2020.113324
中图分类号
O414.1 [热力学];
学科分类号
摘要
Wind energy is renewable and clean energy. To improve the utilization efficiency of the wind energy, studies on wind energy forecasting have gradually been developed in recent years. In these studies, multi-objective optimization technologies have been widely discussed and developed, which can simultaneously improve forecasting performance in multiple aspects, such as accuracy and stability. In this paper, a comprehensive review on the multi-objective optimization technologies in the wind energy forecasting is made. This paper first briefly introduces the basic theories and methods related to the multi-objective optimization. Subsequently, according to different applications, the classification of objective functions is discussed. After describing the applications of the multi-objective optimization, the optimization results and performance improvements obtained in the references are summarized and compared. Finally, the future development of multi-objective optimization in the wind energy forecasting is given.
引用
收藏
页数:22
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