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Title
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Deep learned recurrent type-3 fuzzy system: Application for renewable energy modeling/prediction
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Type
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JournalPaper
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Keywords
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Fuzzy logic Renewable energy Learning algorithm Deep learning Solar energy Wind turbines
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Abstract
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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.
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Researchers
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Shahab S (Fifth Researcher), Amirhosein Mosavi (Not In First Six Researchers), Sakthivel Rathinasamy (Fourth Researcher), Ardashir Mohammadzadeh (Third Researcher), Amir Raise (Second Researcher), Yan Cao (First Researcher)
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