Emergent electricity customer classification

Chicco, Gianfranco; Napoli, Roberto; Piglione, Federico; Postolache, Petru; Scutariu, M.; Toader, Cornel

doi:10.1049/ip-gtd:20041243

Cited by 56 publications

(54 citation statements)

References 26 publications

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“…Utilizing the CDI, the FDL leads to lower errors for large number of clusters (i.e., above 22) while average linkage hierarchical clustering is more efficient for small number. The analysis of [63] is enriched in [66] by adding 2 measures, namely the Scatter Index (SI) and the Variance Ratio Criterion (VRC). Again, the extraction of the optimal algorithm is a matter of the selection of the validity indicator.…”

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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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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“…In the literature, data mining techniques such as clustering and classification algorithms are popular as in [2]. Prevalent in both [1] and [2] is the concept of obtaining representative sets of load profiles based on customer and meteorological conditions. Work done in [4] presents a framework that also uses a data mining approach but with additional indices for classification based on the time of day.…”

Section: Related Workmentioning

confidence: 99%

“…There are several studies that perform customer classification based mostly on load patterns without a preliminary demographic study [1] [2]. This investigation aims to determine whether an in-house classification of customers is useful at the distribution level.…”

Section: Introductionmentioning

confidence: 99%

Analysis of customer profiles on an electrical distribution network

Akperi

Matthews

2014

2014 49th International Universities Power Engineering Conference (UPEC)

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any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Additional information:Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. Abstract-It has become increasingly important for electrical distribution companies to understand the drivers of demand. The maximum demand at any given substation can vary materially on an annual basis which means it is difficult to create a load related investment plan that is robust and stable. Currently, forecasts are based only on historical demand with little understanding about contributions to load profiles. In particular, the unique diversity of customers on any particular substation can affect load profile shape and future forecasts. Domestic and commercial customers can have very different behaviours generally and within these groups there is room for variation due to economic conditions and building types. This paper analyses customer types associated to substations on a distribution network by way of principal component analysis and identification of substations which deviate from the national demand trend. By examining the variance spread of this deviation, data points can be labelled in the principal component space. Groups of substations can then be categorised as having typical or atypical load profiles. This will support the need for further investigation into particular customer types and highlight the key factors of customer categorisation.

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“…Trata-se de um procedimento bastante útil para a resolução de problemas abordados na operação (Chicco et al, 2005;Levi et al 2005), bem como no planejamento. Ou seja, visa reduzir o volume de processamento e a dimensão do conjunto de dados para o treinamento e análises, quando se emprega, por exemplo, outras metodologias neurais para a resolução de problemas como: análise de estabilidade transitória (Ferreira et al, 2006;Sawhney et al 2006), análise de estabilidade de tensão, previsão de cargas elétricas, etc.…”

Section: Introductionunclassified

Formação De Classes De Pefis De Geração E De Carga Em Sistemas Elétricos Usando Uma Rede Neural Nebulosa

Trindade¹,

Minussi²

2016

Anais Do 8. Congresso Brasileiro De Redes Neurais

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Abstract This work presents a methodology for generation and load profiles clustering formation based on fuzzy-ART (Adaptive Resonance Theory) neural network. It is a method that seeks to form the similar groups of nodal electric power, with the purpose of reducing the computacional effort used in the resolution of a series of studies accomplished in electric power systems, e.g., transient stability analysis, voltage stabiliy analysis, distribution system planning, etc. The ART architectures present plasticity and stability characteristics, which are very important for the training and to execute the analysis in a fast way, allowing the continuous training. The continuous training consists in the inclusion the new patterns, if availables, in an appropriate way, on the weight matrix of the ART neural network. This was the main reason of the use this neural network ART-family. To illustrate the proposed neural network an application is presented for a multi-machine electric power systems composed of 10 synchronous machines, 45 buses and 73 transmission lines.Keywords Electric Power Systems, Clustering Technique, Nodal Electric Power Injection, Neural Networks, Adaptive Resonance Theory.Resumo Este artigo apresenta uma metodologia para a formação de classes de perfis de geração e de carga em sistemas de ene rgia elétrica baseada numa rede neural ART (Adaptive Resonance Theory) nebulosa. Trata-se de um procedimento que visa agrupar os vetores referentes às injeções de potência nodais por conjuntos similares, com o propósito de reduzir o esforço computacional empregado na resolução de uma série de estudos realizados em sistemas elétricos de potência, e.g., análise de estabilidade transitória, análise de estabilidade de tensão, planejamento de sistemas de distribuição, etc. As arquiteturas ART apresentam as características de estabilidade e de plasticidade, as quais são imprescindíveis para a realização do treinamento e execução da análise de forma rápida e continuada, ou seja, toda vez que houver disponibilidade de novos padrões, não haverá necessidade de reiniciar o treinamento, bastando apenas armazenar tais padrões, de forma adequada, na memória da rede neural (conjunto de pesos). Esta foi a principal razão do emprego desta rede neural da família ART-descendente. Como forma de ilustrar a estrutura neural proposta, apresenta-se uma aplicação considerando-se um sistema elétrico composto por 10 máquinas síncronas, 45 barras e 73 linhas de transmissão. Palavras-chave

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Emergent electricity customer classification

Cited by 56 publications

References 26 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

Analysis of customer profiles on an electrical distribution network

Formação De Classes De Pefis De Geração E De Carga Em Sistemas Elétricos Usando Uma Rede Neural Nebulosa

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