Deep learned recurrent type-3 fuzzy system: Application for renewable energy modeling/prediction

被引：71

作者：

Cao, Yan ^{[1
]}

Raise, Amir ^{[2
]}

Mohammadzadeh, Ardashir ^{[3
]}

Rathinasamy, Sakthivel ^{[4
]}

Band, Shahab S. ^{[5
]}

Mosavi, Amirhosein ^{[6
]}

机构：

[1] Xian Technol Univ, Sch Mech Engn, Xian 710021, Peoples R China

[2] Xian Technol Univ, Dept Mech Engn, Xian, Shaanxi, Peoples R China

[3] Univ Bonab, Dept Elect Engn, Bonab, Iran

[4] Bharathiar Univ, Dept Appl Math, Coimbatore 641046, Tamil Nadu, India

[5] Natl Yunlin Univ Sci & Technol, Coll Future, Future Technol Res Ctr, 123 Univ Rd,Sect 3, Touliu, Yunlin 64002, Taiwan

[6] Obuda Univ, John von Neumann Fac Informat, H-1034 Budapest, Hungary

来源：

ENERGY REPORTS | 2021年 / 7卷

关键词：

Fuzzy logic; Renewable energy; Learning algorithm; Deep learning; Solar energy; Wind turbines; MANAGEMENT;

D O I：

10.1016/j.egyr.2021.07.004

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：

A deep learned recurrent type-3 (RT3) fuzzy logic system (FLS) with nonlinear consequent part is presented for renewable energy modeling and prediction. Beside the rule parameters, the values of horizontal slices and membership function (MF) parameters are also optimized. The stability of suggested learning scheme is guaranteed. The proposed method is applied for modeling of both solar panels and wind turbines. By the use of experimental setup and generated real-world date sets, the applicability of suggested approach is shown. Comparison with convectional FLSs demonstrates the superiority of the suggested scheme. (C) 2021 Published by Elsevier Ltd.

引用

页码：8115 / 8127

页数：13

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