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An Improved Interval Fuzzy Modeling Method: Applications to the Estimation of Photovoltaic/Wind/Battery Power in Renewable Energy Systems

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  • Nguyen Gia Minh Thao

    (Research Center for Smart Vehicles and Electromagnetic Energy System Laboratory, Toyota Technological Institute, Nagoya 468-8511, Japan)

  • Kenko Uchida

    (Department of Electrical Engineering and Bioscience, Waseda University, Tokyo 169-8555, Japan)

Abstract

This paper proposes an improved interval fuzzy modeling (imIFML) technique based on modified linear programming and actual boundary points of data. The imIFML technique comprises four design stages. The first stage is based on conventional interval fuzzy modeling (coIFML) with first-order model and linear programming. The second stage defines reference lower and upper bounds of data using MATLAB. The third stage initially adjusts scaling parameters in the modified linear programming. The last stage automatically fine-tunes parameters in the modified linear programming to realize the best possible model. Lower and upper bounds approximated by the imIFML technique are closely fitted to the reference lower and upper bounds, respectively. The proposed imIFML is thus significantly less conservative in cases of large variation in data, while robustness is inherited from the coIFML. Design flowcharts, equations, and sample MATLAB code are presented for reference in future experiments. Performance and efficacy of the introduced imIFML are evaluated to estimate solar photovoltaic, wind and battery power in a demonstrative renewable energy system under large data changes. The effectiveness of the proposed imIFML technique is also compared with the coIFML technique.

Suggested Citation

  • Nguyen Gia Minh Thao & Kenko Uchida, 2018. "An Improved Interval Fuzzy Modeling Method: Applications to the Estimation of Photovoltaic/Wind/Battery Power in Renewable Energy Systems," Energies, MDPI, vol. 11(3), pages 1-26, February.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:3:p:482-:d:133180
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    1. Chin-Tan Lee & Shih-Cheng Horng, 2020. "Abnormality Detection of Cast-Resin Transformers Using the Fuzzy Logic Clustering Decision Tree," Energies, MDPI, vol. 13(10), pages 1-19, May.

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