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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.
Appl. Sci. 2018, 8(2), 237;
Received: 3 January 2018 / Revised: 28 January 2018 / Accepted: 31 January 2018 / Published: 4 February 2018
(This article belongs to the Section Energy)
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

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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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