Due to various seasonal and monthly changes in electricity consumption and difficulties in modeling it with the conventional methods, a novel algorithm is proposed in this paper. This study presents an approach that uses Artificial Neural Network (ANN), Principal Component Analysis (PCA), Data Envelopment Analysis (DEA) and ANOVA methods to estimate and predict electricity demand for seasonal a...

In the data envelopment analysis context, problems related to discrimination between efficient and inefficient decision-making units often arise, particularly if there are a relatively large number of variables with respect to observations. This paper presents a comparison of two discrimination-improving methods published in the literature that do not require additional preferential information...

Frontier analysis methods, such as Data Envelopment Analysis (DEA), seek to investigate the technical efficiency of productive systems which employ input factors to deliver outcomes. In economic literature one can find extreme opinions about the role of input/output systems in assessing performance. For instance, it has been argued that if all inputs and outputs are included in assessing the ef...

Yangtze River Delta region in China is rich in port resources. "To prosper city according to ports" has become one of the strategies of promoting economy development. Therefore, the development of port logistics has a very important position in Yangtze River Delta region economy development. This paper sets up a new evaluation index system for port logistics efficiency and effectiveness evaluat...

This article compares two techniques: Data Envelopment Analysis (DEA) and Factor Analysis (FA) to aggregate multiple inputs and outputs in the evaluation of decision making units (DMU). Dِata envelopment analysis (DEA), a popular linear programming technique, is useful to rate comparatively operational efficiency of DMUs based on their deterministic or stochastic input–output data. Factor analys...