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

Research

Title	Deep learned recurrent type-3 fuzzy system: Application for renewable energy modeling/prediction
Type	Article
Keywords	Fuzzy logic Renewable energy Learning algorithm Deep learning Solar energy Wind turbines
Researchers	Yan Cao، Amir Raise، Ardashir Mohammadzadeh، Sakthivel Rathinasamy، Shahab S، Amirhosein Mosavi

Abstract

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.