Measuring Performance Electric Power Generations Using Artificial Neural Networks and Fuzzy Clustering

The efficiency frontier analysis has been an important approach of evaluating firms' performance in private and public sectors. There have been many efficiency frontier analysis methods reported in the literature. However, the assumptions made for each of these methods are restrictive. Each of these methodologies has its strength as well as major limitations. This study proposes a nonparametric efficiency frontier analysis method based on the adaptive neural network technique for measuring efficiency as a complementary tool for the common techniques of the efficiency studies in the previous studies. The proposed computational methods are able to find a stochastic frontier based on a set of input-output observational data and do not require explicit assumptions about the function structure of the stochastic frontier. In purposed algorithm, for calculating the efficiency scores, a similar approach to econometric methods has been used and the effect of the scale of decision making unit (DMU) on its efficiency is included and the unit used for the correction is selected by notice of its scale. For increasing homogeneousness, the algorithm is proposed that use fuzzy C-means method to cluster DMUs. An example using real data is presented for illustrative purposes. In the application to the power generation sector of Iran, we find that the neural network provide more robust results to rank decision making units than the conventional methods