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

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

1
Department of Electrical Engineering, Technological Educational Institute of Thessaly, 41110 Larisa, Greece
2
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)
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Abstract

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
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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.

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