Electricity clustering framework for automatic classification of customer loads

Abstract Clustering in energy markets is a top topic with high significance on expert and intelligent systems. The main impact of is paper is the proposal of a new clustering framework for the automatic classification of electricity customers’ loads. An automatic selection of the clustering classification algorithm is also highlighted. Finally, new customers can be assigned to a predefined set of clusters in the classification phase. The computation time of the proposed framework is less than that of previous classification techniques, which enables the processing of a complete electric company sample in a matter of minutes on a personal computer. The high accuracy of the predicted classification results verifies the performance of the clustering technique. This classification phase is of significant assistance in interpreting the results, and the simplicity of the clustering phase is sufficient to demonstrate the quality of the complete mining framework.

[1]  Antonio Miguel Cruz.  Evaluating record history of medical devices using association discovery and clustering techniques , 2013, Expert Syst. Appl..

[2]  P. Postolache,et al.  Load pattern-based classification of electricity customers , 2004, IEEE Transactions on Power Systems.

[3]  Andrew W. H. Ip,et al.  Customer grouping for better resources allocation using GA based clustering technique , 2012, Expert Syst. Appl..

[4]  J. Dunn.  Well-Separated Clusters and Optimal Fuzzy Partitions , 1974 .

[5]  Gheorghe Grigoras,et al.  Knowlegde extraction from Smart Meters for consumer classification , 2014, 2014 International Conference and Exposition on Electrical and Power Engineering (EPE).

[6]  Mikko Kolehmainen,et al.  Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data , 2010 .

[7]  Olli-Pekka Hilmola,et al.  Cluster analysis application for understanding SME manufacturing strategies , 2016, Expert Syst. Appl..

[8]  Enrico Carpaneto,et al.  Electricity customer classification using frequency–domain load pattern data , 2006 .

[9]  Kerstin Kleese van Dam,et al.  Power Grid Data Analysis with R and Hadoop , 2014 .

[10]  Jie Lu,et al.  A New Index and Classification Approach for Load Pattern Analysis of Large Electricity Customers , 2012, IEEE Transactions on Power Systems.

[11]  Michalis Vazirgiannis,et al.  c ○ 2001 Kluwer Academic Publishers. Manufactured in The Netherlands. On Clustering Validation Techniques , 2022 .

[12]  N.D. Hatziargyriou,et al.  Two-Stage Pattern Recognition of Load Curves for Classification of Electricity Customers , 2007, IEEE Transactions on Power Systems.

[13]  Zita Vale,et al.  A data-mining-based methodology to support MV electricity customers’ characterization , 2015 .

[14]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Z. Vale,et al.  An electric energy consumer characterization framework based on data mining techniques , 2005, IEEE Transactions on Power Systems.

[16]  Gianfranco Chicco,et al.  Overview and performance assessment of the clustering methods for electrical load pattern grouping , 2012 .

[17]  José Luis Díez,et al.  Dynamic clustering segmentation applied to load profiles of energy consumption from Spanish customers , 2014 .

[18]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[19]  K. P. Soman,et al.  Apache Spark a Big Data Analytics Platform for Smart Grid , 2015 .

[20]  Canbing Li,et al.  The contributions of cloud technologies to smart grid , 2016 .

[21]  Ali Azadeh,et al.  An integrated fuzzy regression algorithm for energy consumption estimation with non-stationary data: A case study of Iran , 2010 .

[22]  Debora Coll-Mayor,et al.  Future intelligent power grids: Analysis of the vision in the European Union and the United States , 2007 .

[23]  Douglas B. Kell,et al.  Computational cluster validation in post-genomic data analysis , 2005, Bioinform..

[24]  Guy N. Brock,et al.  clValid , an R package for cluster validation , 2008 .

[25]  Chao Shen,et al.  A review of electric load classification in smart grid environment , 2013 .

[26]  Gisung Kim,et al.  Self-adaptive and dynamic clustering for online anomaly detection , 2011, Expert Syst. Appl..

[27]  David Wallom,et al.  Clustering disaggregated load profiles using a Dirichlet process mixture model , 2015 .

[28]  George K. Karagiannidis,et al.  Big Data Analytics for Dynamic Energy Management in Smart Grids , 2015, Big Data Res..

[29]  G. Chicco,et al.  Comparisons among clustering techniques for electricity customer classification , 2006, IEEE Transactions on Power Systems.

[30]  José G. Dias,et al.  Dynamic clustering of energy markets: An extended hidden Markov approach , 2014, Expert Syst. Appl..

[31]  F. Muñoz,et al.  Hopfield K-Means clustering algorithm: A proposal for the segmentation of electricity customers , 2011 .

[32]  Carlos Arthur Mattos Teixeira Cavalcante,et al.  Pattern recognition as a tool to support decision making in the management of the electric sector. Part II: A new method based on clustering of multivariate time series , 2015 .

[33]  Michalis Vazirgiannis,et al.  A density-based cluster validity approach using multi-representatives , 2008, Pattern Recognit. Lett..

[34]  Maria Lorena Tuballa,et al.  A review of the development of Smart Grid technologies , 2016 .

[35]  Karin Alvehag,et al.  Multi-dimensional customer segmentation model for power system reliability-worth analysis , 2014 .

[36]  Rui Xu,et al.  Survey of clustering algorithms , 2005, IEEE Transactions on Neural Networks.