A novel approach for efficiency assessment of conventional power plants based on principal component analysis

Ali Azadeh and Mahmoud Ghiasi Moaser

International Journal of Productivity and Quality Management, 2010, vol. 6, issue 2, 231-248

Abstract: The investigation of performance efficiency and productivity in power generation sector has become a need due to the importance of energy consumption in the world. Several studies have concentrated on the performance assessment of conventional power plants through mathematical and statistical methods. This paper presents a novel approach based on principal component analysis (PCA) for efficiency assessment of conventional power plants. This study considers the previous approaches, namely: data envelopment analysis (DEA) and PCA for ranking of decision-making units (DMUs). The applicability and superiority of the proposed approach is shown for 15 actual conventional power plants. We also applied the proposed approach to other datasets in previous studies to show its advantages. The numerical results showed that the proposed approach provides better solution than previous studies. It has been shown that in some cases the effect of the number of efficient units is contrary to what previous studies have already predicted. Moreover, the results of the novel approach provide better rankings than previous studies.

Keywords: efficiency assessment; data envelopment analysis; DEA; principal component analysis; PCA; correlation; power plants; energy consumption; decision making units; Iran; electricity generation; productivity. (search for similar items in EconPapers)
Date: 2010
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=34407 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijpqma:v:6:y:2010:i:2:p:231-248

Access Statistics for this article

More articles in International Journal of Productivity and Quality Management from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().