Comparisons Among Clustering Techniques for Electricity Customer Classification

Chicco, Gianfranco; Napoli, Roberto; Piglione, Federico

doi:10.1109/tpwrs.2006.873122

Cited by 449 publications

(275 citation statements)

References 24 publications

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“…Reference [15] is one of the detailed literature surveys on comparisons among customer clustering methods such as modified follow-the-leader, hierarchical clustering, k-means and fuzzy k-means algorithm based partitional clustering, and the Kohonen self-organizing map (SOM). In [15], the results of the clustering validity assessment has shown that the modified follow-the leader and the hierarchical clustering emerge as the most effective ones. However, both these methods are not applied in this paper because they have implementation difficulties.…”

Section: A K-means Clusteringmentioning

confidence: 99%

Regression models for demand reduction based on cluster analysis of load profiles

Yamaguchi

Han

Ghatikar

et al. 2009

2009 IEEE PES/IAS Conference on Sustainable Alternative Energy (SAE)

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Abstract-This paper provides new regression models for demand reduction of Demand Response programs for the purpose of ex ante evaluation of the programs and screening for recruiting customer enrollment into the programs. The proposed regression models employ load sensitivity to outside air temperature and representative load pattern derived from cluster analysis of customer baseline load as explanatory variables.The proposed models examined their performances from the viewpoint of validity of explanatory variables and fitness of regressions, using actual load profile data of Pacific Gas and Electric Company's commercial and industrial customers who participated in the 2008 Critical Peak Pricing program including Manual and Automated Demand Response.

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Section: A K-means Clusteringmentioning

confidence: 99%

Regression models for demand reduction based on cluster analysis of load profiles

Yamaguchi

Han

Ghatikar

et al. 2009

2009 IEEE PES/IAS Conference on Sustainable Alternative Energy (SAE)

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“…Chicco et al describe data-mining algorithms and tools for client classification in the electricity Grids [4] but concentrate on methods for finding groups of customers with similar behaviors.…”

Section: Related Workmentioning

confidence: 99%

Extended Resource Management Using Client Classification and Economic Enhancements

Püschel

Borissov

Neumann

et al. 2009

Economic Models and Algorithms for Distributed Systems

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Commercialization of Grid resources will become more and more important as utility computing and the deployment of Grids gains momentum. This results in the necessity to not only base Grid components on technical aspects, but also to include economical aspects in their design. This paper presents a framework that links technical and economical aspects to the management of computational resources. Economic enhancements like dynamic pricing and client classification are introduced based on a technical resource management environment and positioned within this resulting in a proposed architecture for an Economically Enhanced Resource Manager (EERM). The introduced approach is evaluated considering various economic design criteria and example scenarios.

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“…Kim et al used the k-nearest neighbor method to detect specific appliance electrical events [13]. Also, clustering as a main technique to partition data into groups has been used to classify building electricity customers [14], predict future building energy demand [15], and detect abnormal behaviors [16]. Chui et al proposed an appliance signature identification solution using k-means clustering to identify different household appliances [7].…”

Section: Introductionmentioning

confidence: 99%

Classification of Household Appliance Operation Cycles: A Case-Study Approach

Wang

Srinivasan

2015

Energies

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Abstract:In recent years, a new generation of power grid system, referred to as the Smart Grid, with an aim of managing electricity demand in a sustainable, reliable, and economical manner has emerged. With greater knowledge of operational characteristics of individual appliances, necessary automation control strategies can be developed in the Smart Grid to operate appliances in an efficient manner. This paper provides a way of classifying different operational cycles of a household appliance by introducing an unsupervised learning algorithm called k-means clustering. An intrinsic method known as silhouette coefficient was used to measure the classification quality. An identification process is also discussed in this paper to help users identify the operation mode each types of operation cycle stands for. A case study using a typical household refrigerator is presented to validate the proposed method. Results show that the proposed the classification and identification method can partition and identify different operation cycles adequately. Classification of operation cycles for such appliances is beneficial for Smart Grid as it provides a clear and convincing understanding of the operation modes for effective power management.

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Comparisons Among Clustering Techniques for Electricity Customer Classification

Cited by 449 publications

References 24 publications

Regression models for demand reduction based on cluster analysis of load profiles

Regression models for demand reduction based on cluster analysis of load profiles

Extended Resource Management Using Client Classification and Economic Enhancements

Classification of Household Appliance Operation Cycles: A Case-Study Approach

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