Relevant bibliographies by topics / Electricity Customer Grouping

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Electricity Customer Grouping'

Author: Grafiati
Published: 14 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "Electricity Customer Grouping"

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Sasmita, Sasmita, and Siti Muntari. "PENERAPAN ALGORITMA K-MEANS CLUSTERING PADA DATA KELUHAN PELANGGAN PT. PLN PERSERO KOTA PAGAR ALAM." Jurnal Ilmiah Teknosains 9, no. 1/Mei (2023): 9–12. https://doi.org/10.26877/jitek.v9i1/mei.15366.

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In this modern era, all activities and needs of residents are largely influenced by electricity. Electricity is needed because all household appliances use electric power for company needs or residential needs. To improve the service quality of PT. PLN Persero, Pagar Alam City, in order to reduce the number of customer complaints in Pagar Alam City, a data clustering process is needed which is very important because the increase in data is quite significant. The process of grouping data uses K-Means Clustering because this algorithm is suitable for grouping the data. The results of this study
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Asri, Yessy, Dwina Kuswardani, Efy Yosrita, and Ferdinand Hendrik Wullur. "Clusterization of customer energy usage to detect power shrinkage in an effort to increase the efficiency of electric energy consumption." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 1 (2021): 10–17. https://doi.org/10.11591/ijeecs.v22.i1.pp10-17.

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Automatic meter reading (AMR) is a reading system result the measurement of electrical energy consumen, both locally and remotely. The problems faced is the high non-technical shrinkage of AMR customers due to installation, maintenance errors as well as dishonest actions some consumers, this has a major influence on electrical power losses. PT. PLN Disjaya currently faces difficulties having to choose which customers should be checked first, so the field can only find a little damage. The K-means method based on historical electric power usage and determine the most optimal number of groups th
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Tiara Kusuma, Dine, Norashikin Ahmad, Sharifah Sakinah Syed Ahmad, Iriansyah BM Sangadji, and Yozika Arvio. "An efficient clustering approach in electrical energy consumption patterns." Bulletin of Electrical Engineering and Informatics 14, no. 2 (2025): 1168–77. https://doi.org/10.11591/eei.v14i2.8666.

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A comprehensive understanding of electrical energy consumption patterns is essential for strategizing and monitoring the use of energy resources. Industry and business customers of electrical have energy consumption patterns that vary widely depending on the type of industry, business size, and operating hours. This research uses clustering analysis to obtain electrical energy consumption patterns in industrial and business electricity customer groups by grouping data into similar groups. The variables used in this research are daytime, active power (kW), apparent (kVa), and power factor (PF).
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Asri, Yessy, Dwina Kuswardani, Efy Yosrita, and Ferdinand Hendrik Wullur. "Clusterization of customer energy usage to detect power shrinkage in an effort to increase the efficiency of electric energy consumption." Indonesian Journal of Electrical Engineering and Computer Science 22, no. 1 (2021): 10. http://dx.doi.org/10.11591/ijeecs.v22.i1.pp10-17.

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<span>Automatic meter reading (AMR) is a reading system result the measurement of electrical energy consumen, both locally and remotely. The problems faced is the high non-technical shrinkage of AMR customers due to installation, maintenance errors as well as dishonest actions some consumers, this has a major influence on electrical power losses. PT. PLN Disjaya currently faces difficulties having to choose which customers should be checked first, so the field can only find a little damage. The K-means method based on historical electric power usage and determine the most optimal number
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5

Gajowniczek, Krzysztof, Marcin Bator, and Tomasz Ząbkowski. "Whole Time Series Data Streams Clustering: Dynamic Profiling of the Electricity Consumption." Entropy 22, no. 12 (2020): 1414. http://dx.doi.org/10.3390/e22121414.

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Data from smart grids are challenging to analyze due to their very large size, high dimensionality, skewness, sparsity, and number of seasonal fluctuations, including daily and weekly effects. With the data arriving in a sequential form the underlying distribution is subject to changes over the time intervals. Time series data streams have their own specifics in terms of the data processing and data analysis because, usually, it is not possible to process the whole data in memory as the large data volumes are generated fast so the processing and the analysis should be done incrementally using
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Toledo-Orozco, Marco, Carlos Arias-Marin, Carlos Álvarez-Bel, Diego Morales-Jadan, Javier Rodríguez-García, and Eddy Bravo-Padilla. "Innovative Methodology to Identify Errors in Electric Energy Measurement Systems in Power Utilities." Energies 14, no. 4 (2021): 958. http://dx.doi.org/10.3390/en14040958.

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Many electric utilities currently have a low level of smart meter implementation on traditional distribution grids. These utilities commonly have a problem associated with non-technical energy losses (NTLs) to unidentified energy flows consumed, but not billed in power distribution grids. They are usually due to either the electricity theft carried out by their own customers or failures in the utilities’ energy measurement systems. Non-technical energy losses lead to significant economic losses for electric utilities around the world. For instance, in Latin America and the Caribbean countries,
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Užupytė, Rūta, Tomas Babarskis, and Tomas Krilavičius. "The Generation of Electricity Load Profiles Using K-Means Clustering Algorithm." JUCS - Journal of Universal Computer Science 24, no. (9) (2018): 1306–29. https://doi.org/10.3217/jucs-024-09-1306.

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Accurate information about the actual behavior of electricity users is essential to the electricity suppliers in order to ensure efficient decisions in planning pricing, e.g., designing tariffs and load planning. Load profiles of customers is a straightforward source for such data, however it should be analyzed to extract relevant information. Most of the existing techniques are tested with small data sets or over short periods, which does not allow to investigate seasonality influence. We present a new methodology for the grouping of electricity customers based on the similarities of their (h
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Gajowniczek, Krzysztof, Marcin Bator, Tomasz Ząbkowski, Arkadiusz Orłowski, and Chu Kiong Loo. "Simulation Study on the Electricity Data Streams Time Series Clustering." Energies 13, no. 4 (2020): 924. http://dx.doi.org/10.3390/en13040924.

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Currently, thanks to the rapid development of wireless sensor networks and network traffic monitoring, the data stream is gradually becoming one of the most popular data generating processes. The data stream is different from traditional static data. Cluster analysis is an important technology for data mining, which is why many researchers pay attention to grouping streaming data. In the literature, there are many data stream clustering techniques, unfortunately, very few of them try to solve the problem of clustering data streams coming from multiple sources. In this article, we present an al
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Rachmawati, Anggi, and Dedi Nugroho. "ANALISIS SUSUT NON TEKNIS BERDASARKAN <i>LOAD PROFILE </i>DAN JAM NYALA PADA PELANGGAN AMR (<i>AUTOMATIC METR READING</i>) PT PLN (PERSERO) UP3 BIMA." Elektrika 17, no. 1 (2025): 55–61. https://doi.org/10.26623/elektrika.v17i1.11498.

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Technical and non-technical losses occurs when PLN distributes electricity from generators to customers. To reduce this loss, namely by grouping based on running hours and monitoring the customer's Load Profile parameters. Analysis of research is needed to reduce non-technical losses, especially for large power customers who use the AMR system, such as 189 UP3 Bima customers. In this research, 2 customers experienced a decrease in operating hours exceeding 100 hours, with indications of measurement anomalies in these customers. After normalizing the system, there was uncollected electricity us
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Hughes, Suzaan, and Chantal Breytenbach. "Groupons Growth And Globalization Strategy: Structural And Technological Implications Of International Markets." International Business & Economics Research Journal (IBER) 12, no. 12 (2013): 1589. http://dx.doi.org/10.19030/iber.v12i12.8252.

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Groupon is the fastest growing company in the daily deal social e-commerce arena. For this reason, their growth and globalization strategy is of particular interest to any researcher or investor interested in understanding this industry and its potential future growth and development. In this first follow-up article on mergers and acquisitions as Groupons primary growth and globalization strategy, the researchers discuss the structural and technological implications of expanding into developing international markets. The research method utilized in this article was a case study. In a previous
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Dissertations / Theses on the topic "Electricity Customer Grouping"

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IONEL, OCTAVIAN MARCEL. "Ant Colony Clustering Applied to Electricity Customer Grouping." Doctoral thesis, Politecnico di Torino, 2013. http://hdl.handle.net/11583/2510896.

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This Doctoral thesis presents EPACC (Electrical Pattern Ant Colony Clustering), an original algorithm developed to group electrical load patterns on the basis of their shape. The EPACC algorithm is a clustering technique which takes as input the number of clusters and the initial centroid model composed of the less correlated patterns. The initial set of centroids is a guideline for the evolution of the clustering algorithm, with centroids evolving during the iterative process until the stabilization. So, it is necessary to check at the end of the algorithm that the centroids remain in the sam
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Book chapters on the topic "Electricity Customer Grouping"

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Xu, Jingce, Gaoqi Dai, Xinsheng Zhang, and Jianfei Lu. "A missing-data-intensive grouping method for commercial electricity customers based on user portrait." In Advances in Urban Engineering and Management Science Volume 2. CRC Press, 2022. http://dx.doi.org/10.1201/9781003345329-47.

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Conference papers on the topic "Electricity Customer Grouping"

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Savola, Reijo. "Towards Scalable Solutions of Operational Technology Cybersecurity in Smart Energy Networks." In 16th International Conference on Applied Human Factors and Ergonomics (AHFE 2025). AHFE International, 2025. https://doi.org/10.54941/ahfe1006141.

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During the last years, operational technology cybersecurity threat landscape has become wider, due to the increase of digitalization, more sophisticated cyberattacks and increase of ransomware. Dependence on energy and information networking and operational technology inevitably exposes smart energy networks to potential vulnerabilities associated with networking systems. This increases the risk of compromising reliable and secure use of them. Network intrusion by adversaries may lead to a variety of severe consequences from customer information leakage to a cascade of failures, such as massiv
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Journal articles
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