Evaluating different clustering techniques for electricity customer classification

Bidoki, S. M.; Mahmoudi-Kohan, N.; Sadreddini, Mohammad Hadi; Jahromi, Mansoor Zolghadri; Moghaddam, Mohsen Parsa

doi:10.1109/tdc.2010.5484234

Cited by 18 publications

(16 citation statements)

References 11 publications

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“…References [68][69][70][71] propose two initialization methods in order to enhance the performance of the K-means. The first approach is the Weighted Fuzzy Average (WFA) K-means.…”

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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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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“…References [68][69][70][71] propose two initialization methods in order to enhance the performance of the K-means. The first approach is the Weighted Fuzzy Average (WFA) K-means.…”

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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“…Partitional clustering algorithms, like the K-means, Weighted Fuzzy Average (WFA) K-means, Improved Weight Fuzzy Average (IWFA) K-means and modified "follow-the-leader" (Tsekouras et al, 2007;Chicco et al, 2004;Bidaki et al, 2010;Kohan et al, 2009;Cao et al, 2013;Ramos et al, 2013;Kwac et al, 2014).…”

Section: Literature Survey and Contributionsmentioning

confidence: 99%

“…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%

“…Fuzzy clustering algorithms, like the Fuzzy C-Means (FCM) (Tsekouras et al, 2007;Chicco et al, 2004;Bidaki et al, 2010;Chicco and Ilie, 2009;Anuar and Zakaria, 2012;Gerbec et al, 2002b;Zakaria and Lo, 2009;Iglesias and Kastner, 2013).…”

Section: Literature Survey and Contributionsmentioning

confidence: 99%

“…Neural Network (NN) based clustering algorithms, like the SelfOrganizing Map (SOM), Adaptive Vector Quantization (AVQ) trained NN and Hopfield NN (Tsekouras et al, 2007;Chicco et al, 2004;Bidaki et al, 2010;Rodrigues et al, 2003;Chicco and Akilimali, 2009;Chicco and Ilie, 2009;Räsänen et al, 2010;Figueiredo et al, 2005;López et al, 2011;Verdu et al, 2004;Wang et al, 2013).…”

Section: Literature Survey and Contributionsmentioning

confidence: 99%

See 2 more Smart Citations

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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Mining electrical meter data to predict principal building use, performance class, and operations strategy for hundreds of non-residential buildings

Miller

Meggers

2017

Energy and Buildings

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This study focuses on the inference of characteristic data from a data set of 507 non-residential buildings. A two-step framework is presented that extracts statistical, model-based, and pattern-based behavior. The goal of the framework is to reduce the expert intervention needed to utilize measured raw data in order to infer information such as building use type, performance class, and operational behavior. The first step is temporal feature extraction, which utilizes a library of data mining techniques to filter various phenomenon from the raw data. This step transforms quantitative raw data into qualitative categories that are presented in heat map visualizations for interpretation. In the second step, a random forest classification model is tested for accuracy in predicting primary space use, magnitude of energy consumption, and type of operational strategy using the generated features. The results show that predictions with these methods are 45.6% more accurate for primary building use type, 24.3% more accurate for performance class, and 63.6% more accurate for building operations type as compared to baselines.

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Evaluating different clustering techniques for electricity customer classification

Cited by 18 publications

References 11 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

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

Mining electrical meter data to predict principal building use, performance class, and operations strategy for hundreds of non-residential buildings

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