Research on monitoring technology of power stealing behavior in bitcoin mining based on analyzing electric energy data

Kang, Liyan; Shang, Yuwei; Zhang, Muxin; Liao, Liying

doi:10.1016/j.egyr.2022.02.054

Cited by 6 publications

(3 citation statements)

References 6 publications

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“…Based on the revealed dependencies on the input data, the behavior of the model is predicted. Regressions were used in [27][28][29][30][31]; correlation analysis was used in [32][33][34][35][36][37][38][39].…”

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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“…They reported that it eliminates the release of carbon gases into the atmosphere, leading to a substantial reduction in environmental pollution. Kang et al 24 researched monitoring the electricity stealing for mining purposes according to the available data. They assessed the system voltage, electricity consumption of users, and its effect on electricity loss in the network.…”

Section: Literature Surveymentioning

confidence: 99%

The feasibility study of the production of Bitcoin with geothermal energy: Case study

Ehyaei,

Esmaeilion,

Shamoushaki

et al. 2023

Energy Science & Engineering

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In this paper, a multigeneration cycle of electricity, cooling, and Bitcoin whose energy source is geothermal, has been subjected to energy, exergy, and economic analyses. The cycle under consideration includes the steam cycle (upstream cycle), the carbon dioxide cycle (downstream cycle), and the liquid–gas line to absorb the heat dissipated by the carbon dioxide cycle. In this cycle, the steam cycle condenser acts as the carbon dioxide cycle evaporator. Part of the electricity generated by this cycle is used to generate Bitcoins. Energy and exergy efficiencies at baseline (excluding Bitcoin production) are 45.8% and 38.1%, respectively. In this cycle, if more power is spent on producing Bitcoin as a product, the energy and exergy efficiencies of the cycle are reduced. Because Bitcoin itself is not valuable in terms of energy and exergy. Considering the average price of Bitcoin during the years 2015–2022 and if 100% of the electricity generated by the system is spent on Bitcoin production, the payback period in 2018, 2021, and 2022 when the price of Bitcoin is equal to $13,412.4, $21,398.8, and $47,743.0, respectively, are less than the baseline. Therefore, the production of Bitcoin with a variety of renewable energies can be considered as a solution. Of course, it should be noted that large changes in the price of Bitcoin can affect the issue of economic benefit.

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“…At the same time, the factors for achieving these results remain unclear and break the limits of existing methods. The results of the assessment according to existing methods do not provide the necessary support for the management of sustainable and environmentally friendly development of the energy economy (Kang et al, 2022;Ratner et al, 2022;Vu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

The modern capabilities of monitoring of sustainable and environmental development of the energy economy based on big data and datasets

Yankovskaya

Lobova²,

Grigoreva³

et al. 2023

Front. Energy Res.

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Research on monitoring technology of power stealing behavior in bitcoin mining based on analyzing electric energy data

Cited by 6 publications

References 6 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

The feasibility study of the production of Bitcoin with geothermal energy: Case study

The modern capabilities of monitoring of sustainable and environmental development of the energy economy based on big data and datasets

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