A Versatile Clustering Method for Electricity Consumption Pattern Analysis in Households

Hino, Hideitsu; Shen, Hanfei; Motomura, Noboru; Wakao, Shinji; Hayashi, Yasuhiro

doi:10.1109/tsg.2013.2240319

Cited by 65 publications

(43 citation statements)

References 29 publications

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“…For residential houses, GMM was tested by an intra-hour resolution estimation with multiple residential properties [26]. In larger scale, a similar cluster study produced the daily load profile using 500 households in a 2 year period [27]. Further implementations in our study are explained in Section 3.5…”

Section: Page 5 Of 35mentioning

confidence: 99%

“…Because the covariance matrix must be inverted in order to make a prediction, it makes GPR models more computationally expensive than other models [27]. …”

Section: Page 13 Of 35mentioning

confidence: 99%

“…A c c e p t e d M a n u s c r i p t 27 The study presented here uses 5 minute interval data to predict AC electricity usage. AC prediction is benefited from a fine level of data monitoring at intra-hour interval.…”

Section: Page 27 Of 35mentioning

confidence: 99%

See 2 more Smart Citations

A hybrid model approach for forecasting future residential electricity consumption

Dong

Rahman

et al. 2016

Energy and Buildings

165

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Section: Page 5 Of 35mentioning

confidence: 99%

“…Because the covariance matrix must be inverted in order to make a prediction, it makes GPR models more computationally expensive than other models [27]. …”

Section: Page 13 Of 35mentioning

confidence: 99%

See 1 more Smart Citation

A hybrid model approach for forecasting future residential electricity consumption

Dong

Rahman

et al. 2016

Energy and Buildings

165

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“…Other various clustering algorithms are not presented here because we are focusing on the distance between nodes, not the clustering algorithm. We included the GMM distance [8] as a baseline because it defines the distance between nodes. Ward's linkage [27] is used with the result of the GMM, as proposed in [8].…”

Section: Normalized Energy Consumption Time Slotmentioning

confidence: 99%

“…Other clustering methods have also been applied to the clustering of electricity customers. For example, Hino et al [8] proposed use of the Gaussian mixture model (GMM) in clustering. 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.…”

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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m‐ISODATA : Unsupervised clustering algorithm to capture representative scenarios in power systems

Paula

Oliveira

Honório

et al. 2021

Int Trans Electr Energ Syst

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This paper presents an unsupervised clustering algorithm, called modified Iterative Self-Organizing Data Analysis Technique Algorithm (m-ISODATA), to capture representative nonchronological scenarios for representing shortterm uncertainties in power system models. The proposed approach is suitable to automatically obtain the number of scenarios required to fully capture the variability of historical series, avoiding the need of adjusting the number of clusters as in techniques commonly used in the literature. The performance of the m-ISODATA is discussed and compared with Monte Carlo simulation, the well-known k-means, and hierarchical agglomerate clustering algorithms. In addition, the obtained scenarios are applied to a wind-solar-thermal power

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A Versatile Clustering Method for Electricity Consumption Pattern Analysis in Households

Cited by 65 publications

References 29 publications

A hybrid model approach for forecasting future residential electricity consumption

A hybrid model approach for forecasting future residential electricity consumption

Electricity Customer Clustering Following Experts’ Principle for Demand Response Applications

m‐ISODATA : Unsupervised clustering algorithm to capture representative scenarios in power systems

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