An adaptive network based fuzzy inference system-genetic algorithm clustering ensemble algorithm for performance assessment and improvement of conventional power plants

Authors:
A. Azadeh;M. Saberi;M. Anvari;A. Azaron;M. Mohammadi
Affiliations:
Department of Industrial Engineering, Center of Excellence for Intelligent Experimental Mechanics, College of Engineering, University of Tehran, P.O. Box 11365-4563, Iran;Department of Industrial Engineering, Faculty of Engineering, University of Tafresh, Iran and Institute for Digital Ecosystems & Business Intelligence, Curtin University of Technology, Perth, Aust ...;Department of Industrial Engineering, Center of Excellence for Intelligent Experimental Mechanics, College of Engineering, University of Tehran, P.O. Box 11365-4563, Iran and Department of Industr ...;Department of Financial Engineering and Engineering Management, School of Science and Engineering, Reykjavik University, Reykjavik, Iceland;Department of Computer Engineering, Faculty of Engineering, Iran University of Science and Technology, Tehran, Iran
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

Performance measurement and assessment are fundamental to management planning and control activities of complex systems such as conventional power plants. They have received considerable attention by both management practitioners and theorists. There has been several efficiency frontier analysis methods reported in the literature. However, each of these methodologies has its strength and weakness. This study proposes a non-parametric efficiency frontier analysis methods based on adaptive network based fuzzy inference system (ANFIS) and genetic algorithm clustering ensemble (GACE) for performance assessment and improvement of conventional power plants. The proposed ANFIS-GA algorithm is capable to find a stochastic frontier based on a set of input-output observational data and do not require explicit assumptions about the functional structure of the stochastic frontier. Furthermore, it uses a similar approach to econometric methods for calculating the efficiency scores. Moreover, the effect of the return to scale of a power plant on its efficiency is included and the unit used for the correction is selected by notice of its scale. GACE is used to cluster power plants to increase homogeneousness. The proposed approach is applied to a set of actual conventional power plants to show its applicability and superiority. The superiority and advantages of the proposed algorithm are shown by comparing its results against ANN Fuzzy C-means Algorithm and conventional econometric method.