Name
Title | ||
|---|---|---|
An adaptive network based fuzzy inference system-genetic algorithm clustering ensemble algorithm for performance assessment and improvement of conventional power plants |
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.eswa.2010.08.010 | Expert Syst. Appl. |
Keywords | Field | DocType |
actual conventional power plant,genetic algorithm,cluster power plant,conventional econometric method,improvement,conventional power plants,performance assessment,fuzzy inference system-genetic algorithm,efficiency frontier analysis method,adaptive network based fuzzy inference system (anfis),efficiency score,adaptive network,genetic algorithm clustering ensemble (gace),conventional power plant,non-parametric efficiency frontier analysis,power plant,stochastic frontier,ensemble algorithm,complex system,efficient frontier,input output,returns to scale | Complex system,Data mining,Computer science,Performance measurement,Artificial intelligence,Adaptive neuro fuzzy inference system,Cluster analysis,Genetic algorithm,Power station,Fuzzy logic,Algorithm,Efficient frontier,Machine learning | Journal |
Volume | Issue | ISSN |
38 | 3 | Expert Systems With Applications |
Citations | PageRank | References |
8 | 0.46 | 22 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| A. Azadeh | 1 | 342 | 28.60 |
| Morteza Saberi | 2 | 207 | 28.66 |
| M. Anvari | 3 | 8 | 0.46 |
| A. Azaron | 4 | 108 | 8.20 |
| Mohammad Reza Mohammadi | 5 | 26 | 6.71 |