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.
Highlights
IntroductionIn order to upgrade the role of the consumer in the new landscape of power systems, it is essential to measure the load consumption and implement tools for information retrieval [4,5]
Apart from the number of clusters that are needed to be obtained by an algorithm, other parameters may be needed such as number of iterations, threshold values and others
Modern power system community has recognized the need to upgrade the role of the consumer in competitive energy market
Summary
In order to upgrade the role of the consumer in the new landscape of power systems, it is essential to measure the load consumption and implement tools for information retrieval [4,5]. It should be noted that criteria such as voltage level, demographic parameters, type of economic activity, location and others are not sufficient enough to support a solid consumer classification [12]. This fact is recognized by current research leading to the examination of alternative methods to form consumer classes and derive the load profiles of each class [13]
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