A three-stage data envelopment analysis combined with artificial neural network model for measuring the efficiency of electric utilities

The study of efficiency measurement was pioneered by Farnell in 1957. Since then, researchers in this field have developed many models to measure the efficiency of firms. However, there is no universally acceptable model to measure the efficiency of electric utilities due to differences in the selection of measures, countries’ socioeconomic structures, as well as quality and availability of data. The purpose of this study was to measure and rank the efficiency of electric utilities using a Three-Stage Data Envelopment Analysis-Artificial Neural Network (3S DEA-ANN) model. The model is developed using R programming and RStudio software

Measuring Efficiency of Electric Utilities Using Hybrid Algorithm

Electric utility regulators and policy makers implement incentive-based regulations to improve electric utilities’ efficiency and to manage the cost of electricity. However, poorly implemented regulations may produce undesired results such as low reliability and poor quality of service. Purpose- The purpose of this study is to measure the efficiency of electric utilities and benchmark electric utilities in Malaysia against other utilities worldwide. Since benchmarking of electric utilities is a fairly under researched subject, this study will contribute to the existing body of knowledge by improving the understanding of this subject.

Modelling Efficiency of Electric Utilities Using Three Stage Virtual Frontier Data Envelopment Analysis with Variable Selection by Loads Method

Electric utility regulators and policy makers implement incentive-based regulation to improve electric utilities efficiency or to manage the cost of electricity. However, poorly implemented regulation may produce undesired results such as low reliability or poor quality of service. Moreover, the competition within the electricity sector is likely to be low because of the high barriers to entry, vertically integrated electric utilities, and high capital requirements. Therefore, benchmarking exercises allow policy makers and regulators to gauge the relative efficiency of electric utilities and help them to reward or penalize the electric utilities accordingly. In this study, we examined the variables that significantly influence the efficiency of electric utilities and developed an optimum method to measure the efficiency of the electric utilities. The results of the efficiency measurement were then used to rank the electric utilities. The result of this study indicates that there are 13 variables that significantly affect the efficiency score of electric utilities and three stage virtual frontier data envelopment analysis (3S-VF-DEA) is the optimum method to measure the efficiency of the electric utilities