A Novel Electric Power Plants Performance Assessment Technique Based on Genetic Programming Approach

Abstract

This paper presents a novel nonparametric efficiency analysis technique based on the Genetic Programming (GP) in order to measure efficiency of Iran electric power plants. GP model was used to predict the output of power plants with respect to input data. The method, we presented here, is capable of finding a best performance among power plant based on the set of input data, GP predicted results and real outputs. The advantage of using GP over traditional statistical methods is that in prediction with GP, the researcher doesn't need to assume the data characteristic of the dependent variable or output and the independent variable or input. In this proposed methodology to calculate the efficiency scores, a novel algorithm was introduced which worked on the basis of predicted and real output values. To validate our model, the results of proposed algorithm for calculating efficiency rank of power plants were compared to traditional method. Real data was presented for illustrative our proposed methodology. Results showed that by utilizing the capability of input-output pattern recognition of GP, this method provides more realistic results and outperform in identification of efficient units than the conventional methods.

Multivariate data analysis in hydroelectric system maintenance: A decision evaluation case study

“…The proactive approach is designed to minimize reactive maintenance and is often utilized to predict the outcome of categorical variables. Correlation and regression analysis are also employed in public sector assessments [12] in a vertically integrated electric utility to evaluate customer satisfaction, loyalty, and plant performance [13]. The methodology exploits the utilization of pattern recognition techniques and the ability to group maintenance related items with similar characteristics (or compress the information) of several variables into one or two new ones.…”