An assessment of the renewable energy potential using a clustering based data mining method. Case study in Romania

Gheorghe Grigoras and Florina Scarlatache

Energy, 2015, vol. 81, issue C, 416-429

Abstract: This paper presents an assessment of the renewable energy potential in Romania using a clustering based data mining method. The available data on installed capacity, level voltage, type of renewable technology and geographical location the renewable energy potential for electricity generation was mapped into representative zones using K-Means clustering algorithm. For each zone, the potential was assessed on voltage level and renewable energy generation technologies (wind, solar, hydro, biogas, biomass, and cogeneration). The zones obtained can be a useful working tool for retrofitting substations, upgrading of transmission and distribution lines and also for redesigning them at different parameters with respect to the overload. This information may enable the creation of specific programs to improve planning and development of the electric networks in Romania.

Keywords: Data mining; Clustering; Renewable energy potential; Maps; Romania (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544214014261
Full text for ScienceDirect subscribers only

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:eee:energy:v:81:y:2015:i:c:p:416-429

DOI: 10.1016/j.energy.2014.12.054

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().