Genetic Rules Induction Fuzzy Inference System for Classification and Regression Application in Energy Industry
Chin Hooi Tan1, Keem Siah Yap2, Shen Yuong Wong3, Mau Teng Au4, Chong Tak Yaw5, Hwa Jen Yap6
1Chin Hooi Tan, Business Innovation Incubator, Tenaga Nasional Berhad, Malaysia.
2Keem Siah Yap, Department of Electrical and Electronics, Universiti Tenaga Nasional, Malaysia.
3Shen Yuong Wong, Department of Electrical and Electronics Engineering, Xiamen University Malaysia, Malaysia.
4Mau Teng Au, Institute of Power Engineering, Universiti Tenaga Nasional, Malaysia.
5Chong Tak Yaw, Department of Electrical and Electronics, Universiti Tenaga Nasional, Malaysia.
6Hwa Jen Yap, Department of Mechanical Engineering, University of Malaya, Malaysia.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP:4154-4160 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B4923129219/2019©BEIESP | DOI: 10.35940/ijeat.B4923.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Genetic fuzzy system encompasses genetic algorithm and fuzzy logic. It divulges the advantage of optimization with ease of understanding for classification and regression of energy performance of buildings, transformer, and harmonic current in energy industry. This paper presents development of a new rules induction algorithm namely genetic rules induction fuzzy inference system for classification and regression (GRIFISCnR) that combines genetic algorithm with fuzzy logic to facilitate efficient design of building, transformer and harmonic current filter in energy industry using Pittsburgh approach. GRIFISCnR possesses the rules induction capability over other algorithms for multi-class classification and regression problems without compromising on interpretability and accuracy. It manages to strike a balance between interpretability and accuracy, and yield better accuracy with lesser number of rules. It is easier to interpret and understand fuzzy rules as compared to numerical numbers.
Keywords: Fuzzy Inference System; Genetic Algorithm, Harmonic Current.