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This paper highlights usability of PCA based Defuzzification for the improvement of Sugeno Defuzzification method for knowledge modeling. Research presents designing and implementation of an intelligent system for knowledge modeling, classification and defuzzification. Knowledge is the key to management of ecological innovations in electric utilities of power plants. However, knowledge in the process of information gathering has not been modeled in a formalized way. The system has been evaluated by a sub field of power systems domain of electricity marketing in power plants. Although Sugeno defuzzification method is considered to be the most computationally effective, there is uncertainty about the defuzzified output, since it generates a singleton fuzzy values objectively and not well evaluated. A methodology for PCA based Defuzzification for Sugeno type inference systems has been used directly integrated with the principal component analyzer, fuzzy inference engine, knowledge base and user interface. The PCA based defuzzification system has been tested for modelling tacit knowledge for electric utilities in power plants as per renewable energy. The electric utility assessment tool based on a questionnaire to classify electric utilities (wind, biomass, and hydro) in percentages and identify electric utility performance index in power plants. The project highlights usability of fuzzy logic for designing and implementation of an intelligent system by principal component analysis for renewable energy modeling, classification and defuzzification. The experiment was conducted to investigate performance of PCA based approach with the Sugeno type inference systems. The accuracy of the PCA defuzzification approach is 98 %.

Keywords

Sugeno Defuzzification, Principal Component Analysis, Tacit Knowledge, Fuzzy logic, Power plants.

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References

. Zhang, Ridong, (2016) Industrial and Engineering Chemistry Research, v. 55, (22), p. 6465-6474

. Eminli, Mübariz, Guler, Nevin ,(2010), An improved Takagi-Sugeno fuzzy model with multidimensional fuzzy set, , Journal of Intelligent & Fuzzy Systems, vol. 21, no. 5, pp. 277-287,Expert Systems, vol. 19, no. 1, pp. 34-45.

. Chang Su Lee (2009), A Framework of Adaptive T-S type Rough-Fuzzy Inference Systems (ARFIS),Doctor of Philosophy of Engineering, School of Electrical, Electronic and Computer Engineering of The University of Western Australia

. Debbie Richards and Peter Busch, Acquiring and Applying Contextualized Tacit Knowledge, Journal of Information & Knowledge Management Vol. 02, No. 02, pp. 179-190 (2003)

. Johansson, T.B., Kelly, H., Reddy, A.K.N., and Williams, R.H. Renewable energy: Sources for fuels and electricity. United States: N. p., 1993.

. Robert L. Bradley (1998) Renewable Energy: Not Cheap, Not Green?, Strategic Planning for Energy and the Environment, 17:3, 15-21.

. Akar M, (2000), Decentralized techniques for the analysis and control of Takagi-Sugeno fuzzy systems, IEEE Transactions on Fuzzy Systems (Volume: 8, Issue: 6, Dec 2000, pp. 691 – 704

. Chatfied C, (1996), Introduction to Multivariate Analysis, Chapman and Hall

. Richards D, Bush P, (2003). Measuring, Formalizing and Modeling Tacit Knowledge, IEEE/Web Intelligence Conference (WI-2003), Bejing, Internet publication.

. YAGER,R.R.and .P.FILEV(1993).SLIDE:a simple adaptive defuzzification method. IEEE Transactions on Fuzzy Systems 1(1), pp.69–78

. en.wikipedia.org/wiki/Renewable energy.

. REN21. Renewables 2010. Global status report.

. UNEP, Bloomberg, Frankfurt School, Global trends in renewable energy investment; 011.

. Groscurth HM, Kress KP. Fuzzy data compression for energy optimization models. Energy 1998; 23(1):1–9.

. Cai YP, Huang GH, Yang ZF, Tan Q. Identification of optimal strategies for energy management systems planning under multiple uncertainties. Appl. Energy 2009; 86:480–95.

. Ludwig B. On the sustainability of future energy systems. Energy Convers Manag 1997; 38(15– 17):1765–76.

. Kaminaris SD, Tsoutsos TD, Agoris D, Machias AV. Assessing renewables-to electricity systems: a fuzzy expert system model. Energy Policy 2006; 34:1357– 66.

. Kyriakarakos G, Patlitzianas K, Damasiotis M, Papastefanakis D. A fuzzy cognitive maps decision support system for renewables local planning.

. Renew Sustain Energy Rev 2014; 39:209–22.

. Mamlook R, Akash BA, Mohsen MS. A neuro-fuzzy program approach for evaluating electric power generation systems. Energy 2001; 26:619–32.

. Agrawal RK, Singh SP. Energy allocations for cooking in UP households (India): a fuzzy multiobjective analysis. Energy Convers Manag 2001; 42:2139–54.

. Sunny Thukra, Jatinder Singh Bal, Medical Applications on Fuzzy Logic Inference System: A Review Int. J. Advanced Networking and Applications Volume: 10 Issue: 04 Pages: 3944-3950 (2019) ISSN: 0975-0290

. K. Kalaiselvi, J. Abdul Samath, Survey on Electricity Consumption using Data Mining Techniques, Int. J. Advanced Networking and Applications Volume: 10 Issue: 04 Pages: 3974-3980 (2019) ISSN: 0975-0290

. https://www.energy.gov.lk/images/energybalance/energy-balance-2017.pdf

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