Data size reduction with symbolic aggregate approximation for electrical load pattern grouping

Notaristefano, Antonio; Chicco, Gianfranco; Piglione, Federico

doi:10.1049/iet-gtd.2012.0383

Cited by 66 publications

(36 citation statements)

References 34 publications

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“…Hierarchical agglomerative clustering is used in [34,35]. In [34], the single distance hierarchical algorithm is applied to the daily load curves of a Brazilian hospital.…”

Section: Literature Survey and Contributionsmentioning

confidence: 99%

“…The scope is to exploit the load profiling outcome to build an energy management system for the hospital. In [35], the authors propose a new load data representation technique different from the time-domain one, using symbolic representation of the demand (i.e., letter characters). While the proposed representation technique seems promising in terms of expressing the daily load curves with a reduced set of features, it does not provide the optimal results in terms of low clustering compared with Sammon mapping, principal component analysis and others.…”

Section: Literature Survey and Contributionsmentioning

confidence: 99%

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

Panapakidis

Christoforidis

2018

Applied Sciences

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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.

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“…Hierarchical agglomerative clustering is used in [34,35]. In [34], the single distance hierarchical algorithm is applied to the daily load curves of a Brazilian hospital.…”

Section: Literature Survey and Contributionsmentioning

confidence: 99%

Section: Literature Survey and Contributionsmentioning

confidence: 99%

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

Panapakidis

Christoforidis

2018

Applied Sciences

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“…Full mathematical outline of the four methods (PCA, SM, CCA and CVA) can be found in [13][14][15]. Finally, in [16] the symbolic aggregate representation approximation method is used to obtain a reduced set of features for the data. However, the method is not included in our analysis since it applies only with hierarchical clustering algorithms as stated by the authors.…”

Section: Iet Generation Transmission and Distributionmentioning

confidence: 99%

Enhancing the clustering process in the category model load profiling

Panapakidis

Alexiadis

Papagiannis

2015

IET Generation, Transmission & Distribution

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Load profiling based consumer categorisation is a procedure that involves stages such as the proper representation of the load data, the selection of one or more pattern recognition algorithms, the assessment of their performance, the formation of daily load curve clusters for each consumer and the formation of the final consumer clusters. This study proposes improvements for every stage, while a new stage is introduced to enhance the whole procedure. All the pattern representation techniques of the load profiling related literature are examined together with the most widely used clustering algorithm, namely the K-means. Two novel modified forms of the algorithm are introduced, specially designed by the authors for the problem under study. The aim is to optimally group together similar daily demand patterns to provide exploitable information to the utilities and the retailers for the efficient design and implementation of innovative energy services, targeted to the different consumer categories.

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“…Hierarchical clustering algorithms, like the family of agglomerative algorithms and the COBWEB (Tsekouras et al, 2007;Chicco et al, 2004;Bidaki et al, 2010;Gerbec et al, 2002aGerbec et al, , 2002bGerbec et al, , 2003Nizar et al, 2006;Hino et al, 2013;Notaristefano et al, 2013;Kwac et al, 2014).…”

Section: Literature Survey and Contributionsmentioning

confidence: 99%

“…Medium voltage consumers (Tsekouras et al, 2007;Chicco et al, 2004;Bidaki et al, 2010;Kohan et al, 2009;Gerbec et al, 2002aGerbec et al, , 2002bGerbec et al, , 2003Chicco and Akilimali, 2009;Chicco and Ilie, 2009;Notaristefano et al, 2013;Chicco et al, 2013;Ramos et al, 2013;Wang et al, 2013).…”

Section: Literature Survey and Contributionsmentioning

confidence: 99%

Evaluation of the performance of clustering algorithms for a high voltage industrial consumer

Panapakidis

Alexiadis

Papagiannis

2015

Engineering Applications of Artificial Intelligence

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Data size reduction with symbolic aggregate approximation for electrical load pattern grouping

Cited by 66 publications

References 34 publications

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

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

Enhancing the clustering process in the category model load profiling

Evaluation of the performance of clustering algorithms for a high voltage industrial consumer

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