Next Article in Journal
Plasmonic Physics of 2D Crystalline Materials
Previous Article in Journal
Repeater Insertion for Multi-Walled Carbon Nanotube Interconnects
Article Menu

Export Article

Open AccessArticle
Appl. Sci. 2018, 8(2), 237; doi:10.3390/app8020237

Optimal Selection of Clustering Algorithm via Multi-Criteria Decision Analysis (MCDA) for Load Profiling Applications

Department of Electrical Engineering, Technological Educational Institute of Thessaly, 41110 Larisa, Greece
Department of Electrical Engineering, Western Macedonia University of Applied Sciences, 50100 Kozani, Greece
Author to whom correspondence should be addressed.
Received: 3 January 2018 / Revised: 28 January 2018 / Accepted: 31 January 2018 / Published: 4 February 2018
(This article belongs to the Section Energy)
View Full-Text   |   Download PDF [2308 KB, uploaded 6 February 2018]   |  


Due to high implementation rates of smart meter systems, considerable amount of research is placed in machine learning tools for data handling and information retrieval. A key tool in load data processing is clustering. In recent years, a number of researches have proposed different clustering algorithms in the load profiling field. The present paper provides a methodology for addressing the aforementioned problem through Multi-Criteria Decision Analysis (MCDA) and namely, using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). A comparison of the algorithms is employed. Next, a single test case on the selection of an algorithm is examined. User specific weights are applied and based on these weight values, the optimal algorithm is drawn. View Full-Text
Keywords: clustering; load forecasting; load profiling; MCDA; TOPSIS clustering; load forecasting; load profiling; MCDA; TOPSIS

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Panapakidis, I.P.; Christoforidis, G.C. Optimal Selection of Clustering Algorithm via Multi-Criteria Decision Analysis (MCDA) for Load Profiling Applications. Appl. Sci. 2018, 8, 237.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top