Hopfield–K-Means clustering algorithm: A proposal for the segmentation of electricity customers

• Published in: Electric power systems research Vol. 81; no. 2; pp. 716 - 724
• Main Authors: López, José J., Aguado, José A., Martín, F., Muñoz, F., Rodríguez, A., Ruiz, José E.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.02.2011; Elsevier
• Subjects: Applied sciences; Customer characterization; Electrical engineering. Electrical power engineering; Electrical power engineering; Electricity markets; Exact sciences and technology; Neural networks; Operation. Load control. Reliability; Power networks and lines; Neural networks; Electricity markets; Customer characterization; Autonomous system; Recurrent neural nets; Automatic classification; Segmentation; Clientele; Power system economics; Neural network; Electric utility; Algorithm; Power markets; Hierarchical system; Tariffication; Comparative study; Hopfield model
• ISSN: 0378-7796; 1873-2046
• DOI: 10.1016/j.epsr.2010.10.036

Customer classification aims at providing electric utilities with a volume of information to enable them to establish different types of tariffs. Several methods have been used to segment electricity customers, including, among others, the hierarchical clustering, Modified Follow the Leader and K-Means methods. These, however, entail problems with the pre-allocation of the number of clusters (Follow the Leader), randomness of the solution (K-Means) and improvement of the solution obtained (hierarchical algorithm). Another segmentation method used is Hopfield's autonomous recurrent neural network, although the solution obtained only guarantees that it is a local minimum. In this paper, we present the Hopfield–K-Means algorithm in order to overcome these limitations. This approach eliminates the randomness of the initial solution provided by K-Means based algorithms and it moves closer to the global optimun. The proposed algorithm is also compared against other customer segmentation and characterization techniques, on the basis of relative validation indexes. Finally, the results obtained by this algorithm with a set of 230 electricity customers (residential, industrial and administrative) are presented.