Statistical Fuzzy Inference System for Tacit knowledge modeling in Biomass Energy

----------------------------------------------------------------------ABSTRACT-------------------------------------------------------------- This paper highlights usability of statistical fuzzy inference systems based on PCA for tacit knowledge modeling in Biomass energy. Tacit knowledge is the key to management of ecological innovations in electric utilities of biomass power plants. However, tacit knowledge in the process of information gathering has not been implemented in a formalized way. Although the Sugeno defuzzification method is considered to be the most computationally effective, there is uncertainty about the defuzzified output, since it generates singleton fuzzy values objectively and is not well evaluated. A methodology enhancing the ability of statistical fuzzy inference systems for the improvement of tacit knowledge modeling. This has been exploited in the process of the new approach. The statistical inference system has been used directly integrated with the principal component analyzer, fuzzy inference engine, knowledge base and user interface. The system has been evaluated by a sub field of power systems domain of electricity marketing in biomass power plants. Approach evaluates performance of Biomass energy available for electricity generation. The has been built based on the proposed model and tacit data sets in electricity generation to be used to verify the accuracy of the model and the prototype. The electric utility assessment tool based on a questionnaire to classify electric utilities (agricultural residues, Forest waste, and Household waste) in percentages and identify electric utility performance index in biomass power plants. The experiments were conducted to investigate performance of PCA based Statistical Fuzzy Inference System based on a pilot survey. Output of the system is for computing biomass performance index based on fuzzy output values (agricultural residues, forest waste, household waste) has been generated by PCA based defuzzification process. The accuracy of the PCA based Statistical Fuzzy Inference System approach is 97%.