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

A novel Nonlinear Consequent Part Recurrent Type-2 Fuzzy System (NCPRT2FS) is presented for the modeling of renewable energy systems. Not only does this paper present a new architecture of the type-2 fuzzy system (T2FS) for identification and behavior prognostication of an experimental solar cell set and a wind turbine, but also, it introduces an exquisite technique to acquire an optimal number of membership functions (MFs) and their corresponding rules. Using nonlinear functions in the “Then” part of fuzzy rules, introducing a new mechanism in structure learning, using an adaptive learning rate and performing convergence analysis of the learning algorithm are the innovations of this paper. Another novel innovation is using optimization techniques (including pruning fuzzy rules, initial adjustment of MFs). Next, a solar photovoltaic cell and a wind turbine are deemed as case studies. The experimental data are exploited and the consequent yields emerge as convincing. The root-mean-square-error (RMSE) is less than 0.006 and the number of fuzzy rules is equal to or less than four rules, which indicates the very good performance of the presented fuzzy neural network. Finally, the obtained model is used for the first time for a geographical area to examine the feasibility of renewable energies.

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Adoption of Renewable Energy Technologies: A Fuzzy System Dynamics Perspective

Understanding Complex Systems - Energy Policy Modeling in the 21st Century ◽

10.1007/978-1-4614-8606-0_10 ◽

2013 ◽

pp. 175-196 ◽

Cited By ~ 2

Author(s):

Michael Mutingi

Keyword(s):

Renewable Energy ◽

System Dynamics ◽

Fuzzy System ◽

Renewable Energy Technologies ◽

Energy Technologies

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Hybrid neuro-fuzzy system application to inferential sensing

2010 5th IEEE Conference on Industrial Electronics and Applications ◽

10.1109/iciea.2010.5514788 ◽

2010 ◽

Author(s):

S. Jassar ◽

L. Zhao ◽

Z. Liao

Keyword(s):

System Application ◽

Fuzzy System ◽

Neuro Fuzzy

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A New Data-Driven Control System for MEMSs Gyroscopes: Dynamics Estimation by Type-3 Fuzzy Systems

Micromachines ◽

10.3390/mi12111390 ◽

2021 ◽

Vol 12 (11) ◽

pp. 1390

Author(s):

Khalid A. Alattas ◽

Ardashir Mohammadzadeh ◽

Saleh Mobayen ◽

Ayman A. Aly ◽

Bassem F. Felemban ◽

...

Keyword(s):

Fuzzy System ◽

Measurement Errors ◽

Fuzzy Systems ◽

Data Driven ◽

Output Data ◽

Data Set ◽

Control Scheme ◽

Mems Gyroscopes ◽

The Stability ◽

Type 3

In this study, a novel data-driven control scheme is presented for MEMS gyroscopes (MEMS-Gs). The uncertainties are tackled by suggested type-3 fuzzy system with non-singleton fuzzification (NT3FS). Besides the dynamics uncertainties, the suggested NT3FS can also handle the input measurement errors. The rules of NT3FS are online tuned to better compensate the disturbances. By the input-output data set a data-driven scheme is designed, and a new LMI set is presented to ensure the stability. By several simulations and comparisons the superiority of the introduced control scheme is demonstrated.

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Case Study

International Journal of 3-D Information Modeling ◽

10.4018/ij3dim.2013100103 ◽

2013 ◽

Vol 2 (4) ◽

pp. 25-37 ◽

Cited By ~ 2

Author(s):

Aydin Tabrizi ◽

Paola Sanguinetti

Keyword(s):

Renewable Energy ◽

Performance Enhancement ◽

Energy Modeling ◽

Information Modeling ◽

Weather Data ◽

Building Performance ◽

Net Energy ◽

Photovoltaic Panel ◽

Building Information

This case study focuses on the operational performance of a Leadership in Energy & Environmental Design (LEED)-rated building with the application of Building Information Modeling (BIM) to evaluate its capacity to achieve Zero Net Energy (ZNE). Retrofit options for renewable energy implementation are examined in conjunction with scenarios of building operation. In this study, two different BIM processes have been conducted for the energy modeling: object-oriented geometric information modeling (e.g., envelope, doors, windows, walls, zones, etc.) with a BIM tool and energy modeling (e.g., materials, heat resistance, location, weather data, renewables, etc.) with an energy simulation tool. The simulation model is compared to the real building performance and alternative renewable energy scenarios are evaluated. The results are used to make recommendations for the optimization of building performance and consideration of energy-efficient strategies for building performance enhancement. The research points to discontinuities between photovoltaic panel degradation over time and the LEED credit.

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