Electricity theft whistleblowing feasibility in commercial accommodation facilities

Adongo, Charles Atanga; Taale, Francis; Bukari, Shaibu; Suleman, Shafic; Amadu, Iddrisu

doi:10.1016/j.enpol.2021.112347

Cited by 7 publications

(3 citation statements)

References 47 publications

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“…Consider a situation in a network with a distributed load: if a specific electricity user's meter readings decrease, yet no similar decline is registered on the state metering device placed on the outgoing connection or at the entrance of a 10/0.4 kV transformer substation, one can infer that the user has tampered with their metering apparatus. A substantial set of such rules has been put forward in research, with several elaborated in [34,[49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65].…”

Section: Methods Of Identification and Assessment Of Nontechnical Los...mentioning

confidence: 99%

Data-Driven Machine Learning Methods for Nontechnical Losses of Electrical Energy Detection: A State-of-the-Art Review

Pazderin,

Kamalov,

Gubin

et al. 2023

Energies

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Nontechnical losses of electrical energy (NTLEE) have been a persistent issue in both the Russian and global electric power industries since the end of the 20th century. Every year, these losses result in tens of billions of dollars in damages. Promptly identifying unscrupulous consumers can prevent the onset of NTLEE sources, substantially reduce the amount of NTLEE and economic damages to network grids, and generally improve the economic climate. The contemporary advancements in machine learning and artificial intelligence facilitate the identification of NTLEE sources through anomaly detection in energy consumption data. This article aims to analyze the current efficacy of computational methods in locating, detecting, and identifying nontechnical losses and their origins, highlighting the application of neural network technologies. Our research indicates that nearly half of the recent studies on identifying NTLEE sources (41%) employ neural networks. The most utilized tools are convolutional networks and autoencoders, the latter being recognized for their high-speed performance. This paper discusses the main metrics and criteria for assessing the effectiveness of NTLEE identification utilized in training and testing phases. Additionally, it explores the sources of initial data, their composition, and their impact on the outcomes of various algorithms.

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Section: Methods Of Identification and Assessment Of Nontechnical Los...mentioning

confidence: 99%

Data-Driven Machine Learning Methods for Nontechnical Losses of Electrical Energy Detection: A State-of-the-Art Review

Pazderin,

Kamalov,

Gubin

et al. 2023

Energies

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show abstract

“…Other efforts, such as awareness campaigns on electricity theft, have been explored as stated by Chetty [6]. Furthermore, Adongo, et al [7] proposed a model for whistleblowing on electricity theft to contribute to mitigating the challenge. Moreover, technology-based models have been Electricity 2024, 5 explored Yan and Wen [8], Tanwar, et al [9], Zheng, et al [10].…”

Section: Introductionmentioning

confidence: 99%

Electricity Theft Detection and Prevention Using Technology-Based Models: A Systematic Literature Review

Kgaphola,

Marebane,

Hans

2024

Electricity

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Electricity theft comes with various disadvantages for power utilities, governments, businesses, and the general public. This continues despite the various solutions employed to detect and prevent it. Some of the disadvantages of electricity theft include revenue loss and load shedding, leading to a disruption in business operations. This study aimed to conduct a systematic literature review to identify what technology solutions have been offered to solve electricity theft and the effectiveness of those solutions by considering peer-reviewed empirical studies. The systematic literature review was undertaken following the guidelines for conducting a literature review in computer science to assess potential bias. A total of 11 journal articles published from 2012 to 2022 in SCOPUS, Science Direct, and Web of Science were analysed to reveal solutions, the type of theft addressed, and the success and limitations of the solutions. The findings show that the focus in research is channelled towards solving electricity theft in Smart Grids (SGs) and Advanced Metering Infrastructure (AMI); moreover, there is a neglect in the recent literature on finding solutions that can prevent electricity theft in countries that do not have SG and AMI installed. Although the results reported in this study are confined to the analysed research papers, the leading limitation in the selected studies, lack of real-life data for dishonest users. This study’s contribution is to show what technology solutions are prevalent in solving electricity theft in recent years and the effectiveness of such solutions.

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“…As a result, the capacity of the country's power companies to generate revenue and efficiently distribute power is being negatively impacted. Electricity theft is one of the most critical sources of non-technical losses in the electricity sector, of which the citizens are significant perpetrators (Adongo et al, 2021). Accordingly, the trend not only decreases electricity revenues but portends risks to power usage's safety, which has been increasingly challenging nowadays (Feng et al, 2020) Electricity theft can be fraud, such as meter tampering, illegal connections, billing irregularities, and unpaid bills (Smith, 2004).…”

Section: Introductionmentioning

confidence: 99%

Electricity Consumers and Electricity Theft in Edo State, Nigeria: Implication for Efficient Power Distribution

Ogbe¹

2023

NN-EEE

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Over the years, Nigeria has faced the challenges of inadequate generation, supply, and distribution of power. There is growing concern about the pervasiveness of electricity theft in the country, suggested to be among the chief factors inhibiting electricity sector efficiencies. The present study aimed to ascertain the trend of electricity theft among electricity consumers in Edo state, Nigeria. The study adopted a survey method to examine the prevalence of electricity theft among households in randomly selected towns in Edo state, Nigeria. The study population comprised electricity consumers under the residential category described by the BEDC. A convenient sample of 217 residential consumers and BEDC officials completed the research questionnaires. The finding shows that 32.7% of the electricity consumers indicated they never participated in energy theft, while 67.3% showed high involvement. A linear regression analysis was conducted to answer whether electricity theft affects efficient power distribution in Edo state. The result demonstrated a statistically significant effect of electricity theft on efficient power distribution in Edo state F (1, 12), 21.36 P< .05. Also, the R2 indicated that electricity theft contributed about 54.4% of the inefficiencies in power distribution in Edo state. Thus, it is concluded that the trend of electricity theft is high in the area of study and significantly affects the efficient distribution of electricity. The result provides valuable data to the government and electricity regulators.

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Electricity theft whistleblowing feasibility in commercial accommodation facilities

Cited by 7 publications

References 47 publications

Data-Driven Machine Learning Methods for Nontechnical Losses of Electrical Energy Detection: A State-of-the-Art Review

Data-Driven Machine Learning Methods for Nontechnical Losses of Electrical Energy Detection: A State-of-the-Art Review

Electricity Theft Detection and Prevention Using Technology-Based Models: A Systematic Literature Review

Electricity Consumers and Electricity Theft in Edo State, Nigeria: Implication for Efficient Power Distribution

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