A pattern recognition methodology for evaluation of load profiles and typical days of large electricity customers

Tsekouras, George J.; Kotoulas, P.B.; Tsirekis, C. D.; Dialynas, E.N.; Hatziargyriou, Nikos D.

doi:10.1016/j.epsr.2008.01.010

Cited by 87 publications

(57 citation statements)

References 15 publications

(46 reference statements)

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“…As mentioned at the beginning of this subsection, rows in matrix B can be considered as typical daily load curves. It has been shown in [25] that k-means can efficiently extract typical load curves from smart meter data. Therefore, initializing matrix B with k-means cluster centers is able to improve the convergence rate.…”

Section: End Whilementioning

confidence: 99%

Robust Smart Meter Data Analytics Using Smoothed ALS and Dynamic Time Warping

et al. 2018

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This paper presents a robust data-driven framework for clustering large-scale daily chronological load curves from smart meters, with a focus on the challenges encountered in practice. The first challenge is the low data quality issue due to bad and missing data, which has been a major obstacle for various in-depth analyses of smart meter data. A novel Smoothed Alternating Least Squares (SALS) approach is proposed to recover missing/bad smart meter data by taking advantage of their low-rank property. The second challenge is brought by different data reporting rates of smart meters. A Dynamic Time Warping (DTW)-based approach is proposed that is more efficient and eliminates the need for data interpolation or measurement downsampling. The proposed approach enables flexible data collection strategies and gateway locations to meet various smart grid performance requirements. The proposed framework is tested through experiments using real-world smart meter data.

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Section: End Whilementioning

confidence: 99%

Robust Smart Meter Data Analytics Using Smoothed ALS and Dynamic Time Warping

et al. 2018

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“…Research in this area has proposed many different indicators [22,23], such as the mean index adequacy (MIA), the clustering dispersion indicator (CDI), the similarity matrix indicator (SMI), the Davies-Bouldin indicator (DBI), the modified Dunn index, the scatter index (SI), and the mean square error [24]. Many studies [25] on clustering illustrate the applications and compare the results obtained by various unsupervised clustering algorithms based on these adequacy measures.…”

Section: The Process Of Clustering Analysismentioning

confidence: 99%

Research on the Influence of Several Factors on Clustering Results using Different Algorithms

Zhang¹,

Gu²

2015

TOCSJ

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Clustering analysis is an important technology in the field of pattern extraction and recognition. In order to find the influence of several factors on clustering results using different algorithms and support the decision for power load pattern extraction using clustering techniques, this paper develops the research on the influence of several factors on clustering results using different algorithms. In this paper, three data sets, five normalization methods and five known clustering algorithms including k-means, FCM, SOM, hierarchical clustering and spectral clustering are used, four experiments are designed and performed, they are the influence of the normalization methods on the clustering results, the dependence of clustering results on a data set, the algorithm stability and the sensitivity of clustering algorithm to the input order of the data. The results show that all the factors have obvious influence on the clustering results, and using maximum normalization and FCM algorithm in clustering procedure has the best performance for power load pattern extraction.

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“…In their work, energy consumption graphs were interpreted as a mixture of Gaussian distributions, and the distance between load profiles was defined by the K-L divergence of the Gaussian mixture distributions. In the literature, other various machine learning techniques such as the self-organizing map (SOM), neural network, support vector clustering, dynamic time warping (DTW), and latent Dirichlet allocation (LDA) have also been applied to cluster electricity customers [9][10][11][12][13][14][15][16]. Furthermore, the transformation of input data to other domains such as frequency domain has been proposed in the literature [17].…”

Section: Introductionmentioning

confidence: 99%

Electricity Customer Clustering Following Experts’ Principle for Demand Response Applications

Kang

Lee

2015

Energies

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Abstract:The clustering of electricity customers might have an effective meaning if, and only if, it is verified by domain experts. Most of the previous studies on customer clustering, however, do not consider real applications, but only the structure of clusters. Therefore, there is no guarantee that the clustering results are applicable to real domains. In other words, the results might not coincide with those of domain experts. In this paper, we focus on formulating clusters that are applicable to real applications based on domain expert knowledge. More specifically, we try to define a distance between customers that generates clusters that are applicable to demand response applications. First, the k-sliding distance, which is a new distance between two electricity customers, is proposed for customer clustering. The effect of k-sliding distance is verified by expert knowledge. Second, a genetic programming framework is proposed to automatically determine a more improved distance measure. The distance measure generated by our framework can be considered as a reflection of the clustering principles of domain experts. The results of the genetic programming demonstrate the possibility of deriving clustering principles.

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A pattern recognition methodology for evaluation of load profiles and typical days of large electricity customers

Cited by 87 publications

References 15 publications

Robust Smart Meter Data Analytics Using Smoothed ALS and Dynamic Time Warping

Robust Smart Meter Data Analytics Using Smoothed ALS and Dynamic Time Warping

Research on the Influence of Several Factors on Clustering Results using Different Algorithms

Electricity Customer Clustering Following Experts’ Principle for Demand Response Applications

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