ABSTRACT

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