Dynamic clustering segmentation applied to load profiles of energy consumption from Spanish customers

Benítez, Ignacio; Quijano, Alfredo; Díez, José-Luis; Delgado, Ignacio García

doi:10.1016/j.ijepes.2013.09.022

Cited by 126 publications

(52 citation statements)

References 18 publications

(17 reference statements)

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“…The results of the K-means are combined with homeowner's survey data in order to track correlations between the consumption and other parameters like income, education level and others. In [31], the authors propose via the K-means the concept of dynamic clustering in Spanish residential consumers. This refers to clustering the full load time series (i.e., the total load sequence) instead of using daily curves.…”

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 order to investigate the validity of results obtained by the dynamic K-means clustering, we compare the values of validity function for K ϵ [8,20] in conventional K-means clustering and dynamic K-means clustering, as shown in Fig. (5).…”

Section: The Characteristics Of Datamentioning

confidence: 99%

“…As for the clustering methods with dynamic characteristics, Bhargavi and Gowda [19] develop a clustering validity index to dynamically terminate the clustering process. Benítez et al [20] propose a dynamic clustering segmentation algorithm to profiling of energy load. Ozturk et al [21] use the Artificial Bee Colony (ABC) algorithm to optimize the clustering validity function, without determining the number of clusters in advance.…”

Section: Introductionmentioning

confidence: 99%

A Risk Decision-making Approach to Customs Targeting

Xiao¹,

Xiao²,

Wang³

2016

Open Cyber

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This paper focuses on the risk decision-making problem in customs targeting, whose major responsibility is to inspect the smuggling goods in import/export declarations. In this problem, the estimated smuggling probabilities of import/export goods, which can be obtained by applying statistical analysis to observations (samples), are needed for accurate inspection decision. A critical presumption for statistical analysis is that the samples are homogeneous or subject to certain distributions. Therefore, clustering techniques are usually employed for preprocessing the samples. However, severe heterogeneity and abnormality exist among the large amount of samples and thus hinder the performance of conventional clustering methods for preprocessing. To deal with this problem, a dynamic K-means clustering method is developed in this paper. Through optimizing the validity function that indicates the goodness of clustering result, the entire samples are iteratively divided into a number of clusters. Based on the dynamic K-means clustering method and logistic regression, a risk decision-making approach is proposed and applied to China's customs targeting. The empirical results show that the proposed approach improves the accuracy and decreases the risk of inspection decision.

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“…The usual stated applications range from the design and simulation of demand side management strategies (DSM) [4], [5], load forecasting [6], [7], tariff setting [8,9,10], marketing and bad data detection. The clustering methods found to be used are mostly the K-means algorithm [5,11,12,13,14]. Fuzzy clustering [15] has shown promise in the field.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy clustering and prediction of electricity demand based on household characteristics

Viegas¹,

Vieira²,

Sousa³

2015

Advances in Intelligent Systems Research

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The electricity market has been significantly changing in the last decade. The deployment of smart meters is enabling the logging of huge amounts of data relating to the operations of utilities with the potential of being translated into valuable knowledge on the behaviour of consumers. This work proposes a methodology for predicting the typical daily load profile of electricity usage based on static data using fuzzy clustering and modelling. The methodology intends to: (1) determine consumer segments based on the metering data using the fuzzy c-means clustering algorithm, and (2) develop Takagi-Sugeno fuzzy models in order to predict the demand profile of the consumers.

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Dynamic clustering segmentation applied to load profiles of energy consumption from Spanish customers

Cited by 126 publications

References 18 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

A Risk Decision-making Approach to Customs Targeting

Fuzzy clustering and prediction of electricity demand based on household characteristics

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