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Journal articles on the topic 'Electricity Customer Grouping'

Author: Grafiati

Published: 14 March 2023

Last updated: 31 July 2025

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1

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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2

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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3

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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4

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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6

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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7

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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8

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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9

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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10

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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11

Gajowniczek, Krzysztof, and Tomasz Ząbkowski. "Simulation Study on Clustering Approaches for Short-Term Electricity Forecasting." Complexity 2018 (2018): 1–21. http://dx.doi.org/10.1155/2018/3683969.

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Advanced metering infrastructures such as smart metering have begun to attract increasing attention; a considerable body of research is currently focusing on load profiling and forecasting at different scales on the grid. Electricity time series clustering is an effective tool for identifying useful information in various practical applications, including the forecasting of electricity usage, which is important for providing more data to smart meters. This paper presents a comprehensive study of clustering methods for residential electricity demand profiles and further applications focused on

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12

Touil, Redouane, Rachid Marrakh, Taoufiq Belhoussine Drissi, and Bahloul Bensassi. "Improve customer service quality, reduce operating expenses, and improve energy sales by the K-MEANS method and path optimization algorithms." Data and Metadata 4 (January 1, 2025): 489. http://dx.doi.org/10.56294/dm2025489.

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Managing consumer expectations was essential to maintaining customer satisfaction throughout the electricity contract. However, the service provided to customers was based on the location of electrical meters. In the absence of addressing in rural areas, it was too difficult to ensure a comprehensive survey of electrical meter indexes and intervene in time for troubleshooting. The method adopted was the choice of a site with a significant number of meters and energy transformers and the geolocation of electrical installations by a GPS that allowed the assignment of a universal address to elect

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13

Lemes, Daniel Lima, Matheus Mello Jacques, Natalia Bastos Sousa, et al. "Estimation of Electrical Energy Consumption in Irrigated Rice Crops in Southern Brazil." Energies 16, no. 18 (2023): 6742. http://dx.doi.org/10.3390/en16186742.

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On average, 70% of the world’s freshwater is used in agriculture, with farmers transitioning to electrical irrigation systems to increase productivity, reduce climate uncertainties, and decrease water consumption. In Brazil, where agriculture is a significant part of the economy, this transition has reached record levels over the last decade, further increasing the impact of energy consumption. This paper presents a methodology that utilizes the U-Net model to detect flooded rice fields using Sentinel-2 satellite images and estimates the electrical energy consumption required to pump water for

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14

RYABCHIK, ALEKSEY P., and ANTONINA V. SHARKOVA. "SMALL ENERGY IS A DRIVER OF RUSSIA›S SPATIAL DEVELOPMENT." Scientific Works of the Free Economic Society of Russia 243, no. 5 (2023): 412–32. http://dx.doi.org/10.38197/2072-2060-2023-243-5-412-432.

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Relevance of the topic: One of the possible directions of further development of electric power industry is investment in distributed (small) power industry. It can become an impetus for the development of isolated areas of Russia from the Unified Energy System of Russia, as it will reduce the cost of 1 kWh of electricity and, most importantly, use the resources that this region has, as at the moment it is the lack and high cost of electricity that hinders the development of these regions. Object: is the introduction of small-scale power in Russia. Subject: are the prospects and risks of using

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15

Bintoro, Andik, and Safwandi Safwandi. "KLASIFIKASI PENGELOMPOKAN DALAM MELIHAT KESESUAIAN DAYA PELANGGAN KOTA LHOKSEUMAWE." KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) 2, no. 1 (2018). http://dx.doi.org/10.30865/komik.v2i1.944.

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Classification of K Nearest Neighbors in this study to determine the grouping in seeing the suitability of the installed household electricity customers. Then the system built can see customers who want to know the amount of power given and want to add new. Conversely, if customers who want to reduce the power that has been given because it is too large with the condition of houses that are not large and not much use, can be seen in this system. The purpose of this study is to facilitate old customer customers in seeing the installed power with a variable amount of air conditioner (AC), number

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16

Dine, Tiara Kusuma, Ahmad Norashikin, Sakinah Syed Ahmad Sharifah, BM Sangadji Iriansyah, and Arvio Yozika. "An efficient clustering approach in electrical energy consumption patterns." March 5, 2025. https://doi.org/10.11591/eei.v14i2.8666.

Full text

Abstract:

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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17

Aurangzeb, Khursheed. "Anomalies and major cluster-based grouping of electricity users for improving the forecasting performance of deep learning models." Frontiers in Energy Research 11 (November 15, 2023). http://dx.doi.org/10.3389/fenrg.2023.1284076.

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Analyzing and understanding the electricity consumption of end users, especially the anomalies (outliers), are vital for the planning, operation, and management of the power grid. It will help separate the group of users with unpredictable consumption behavior and then develop and train specialized deep learning models for power load forecasting or regular and non-regular users. The aim of the current work is to divide electricity customers into numerous groups based on anomalies in consumption behavior and major clusters. Successful separation of such groups of customers will provide us with

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18

Kawoosa, Asif Iqbal, Deepak Prashar, G. R. Anantha Raman, et al. "Improving Electricity Theft Detection Using Electricity Information Collection System and Customers’ Consumption Patterns." Energy Exploration & Exploitation, May 25, 2024. http://dx.doi.org/10.1177/01445987241255394.

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Electricity theft detection (ETD) techniques employed to identify fraudulent consumers often fail to accurately pinpoint electricity thieves in real time. The patterns associated with electricity use are leveraged to identify anomalies indicative of electricity theft. However, challenges in the benchmark ETD include overfitting and a high incidence of false positives (FPs) resulting from incorrect usage patterns formed by considering only electricity consumption patterns without accounting for external factors that contribute to variations in normal consumption patterns. Further investigation

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