Load Pattern-Based Classification of Electricity Customers

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

doi:10.1109/tpwrs.2004.826810

Cited by 160 publications

(96 citation statements)

References 6 publications

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“…One of the first studies in this line was by Chicco et al [49], who grouped customers into classes, based on their electricity behavior. A modified version of the follow-the-leader algorithm and self-organizing maps were used to compare the results.…”

Section: Economic Analysis Of Electric Consumptionmentioning

confidence: 99%

Data science for building energy management: A review

Molina-Solana

Ros

Ruiz

et al. 2017

Renewable and Sustainable Energy Reviews

210

108

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The energy consumption of residential and commercial buildings has risen steadily in recent years, an increase largely due to their HVAC systems. Expected energy loads, transportation, and storage as well as user behavior influence the quantity and quality of the energy consumed daily in buildings. However, technology is now available that can accurately monitor, collect, and store the huge amount of data involved in this process. Furthermore, this technology is capable of analyzing and exploiting such data in meaningful ways. Not surprisingly, the use of data science techniques to increase energy efficiency is currently attracting a great deal of attention and interest. This paper reviews how Data Science has been applied to address the most difficult problems faced by practitioners in the field of Energy Management, especially in the building sector. The work also discusses the challenges and opportunities that will arise with the advent of fully connected devices and new computational technologies.

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Section: Economic Analysis Of Electric Consumptionmentioning

confidence: 99%

Data science for building energy management: A review

Molina-Solana

Ros

Ruiz

et al. 2017

Renewable and Sustainable Energy Reviews

210

108

View full text Add to dashboard Cite

show abstract

“…Additionally frequencybased indices and hourly LP curve methods were proposed in [28]. In [29][30], authors have modified the Follow-The-Leader algorithm and proposed a frequencydomain approach with SOMs for consumption patternbased classification of electricity consumers in order to know accurate knowledge of the customers' consumption patterns. Hierarchal clustering was also considered in [31] to determine customers' daily LP in order to cluster the similar units of measured LPs.…”

Section: Related Workmentioning

confidence: 99%

A Mobile-based Platform for Big Load Profiles Data Analytics in Non-Advanced Metering Infrastructures

Moussa

2016

MATEC Web Conf.

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Abstract. With the rapidly increase of electricity demand around the world due to industrialization and urbanization, this turns the availability of precise knowledge about the consumption patterns of consumers to a valuable asset for electricity providers, given the current competitive electricity market. This would allow them to provide satisfactory services in time of load peaks and to control fraud and abuse cases. Despite of this crucial necessity, this is currently very hard to achieve in many developing countries since smart meters or advanced metering infrastructures (AMIs) are not yet settled there to monitor and report energy usages. Whereas the communication and information technologies have widely emerged in such nations, allowing the enormous spread of smart devices among population. In this paper, we present mobile-based BLPDA, a novel platform for big data analytics of consumers' load profiles (LPs) in the absence of AMIs' establishment. The proposed platform utilizes mobile computing in order to collect the consumptions of consumers, build their LPs, and analyze the aggregated usages data. Thus, allowing electricity providers to have better vision for an enhanced decision making process. The experimental results emphasize the effectiveness of our platform as an adequate alternative for AMIs in developing countries with minimal cost.

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“…In power load pattern extraction, we can identify the TLPs of each customer by analyzing the distribution of load curves in the load patterns. 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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Load Pattern-Based Classification of Electricity Customers

Cited by 160 publications

References 6 publications

Data science for building energy management: A review

Data science for building energy management: A review

A Mobile-based Platform for Big Load Profiles Data Analytics in Non-Advanced Metering Infrastructures

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

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