Electricity Theft Detection Base on Extreme Gradient Boosting in AMI

Yan, Zhongzong; Wen, He

doi:10.1109/i2mtc43012.2020.9128712

Cited by 10 publications

(7 citation statements)

References 13 publications

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“…Their work has achieved a plausible accuracy rate of 89% but a lower detection rate (recall) of around 87%. Another technique Hasan et al [5] CNN-LSTM N/A Zanetti et al [6] FCM Hu et al [7] DSN + DAE Gunturi and Sarkar [8] Ensmble ML N/A Yan and Wen [9] XGBoost N/A Our approach Various MLs in [7] has shown a better detection rate where a semisupervised technique was used to train the detection model. In the semi-supervised training, labelled samples and unlabelled samples are used to train a deep Siamese network (DSN) model and de-noising auto-encoder (DAE) respectively.…”

Section: A Related Work and Limitationsmentioning

confidence: 96%

“…In their study, the authors found that a bagging-type ensemble ML approach, which combines the results of independent MLs parallel by taking the average, performs better than a boosting one. Another recent study [9] have used extreme gradient boosting (XGBoost) which is a scalable implementation of decision tree boosting system. The study showed that the XGBoost model is robust when the dataset is imbalanced.…”

Section: A Related Work and Limitationsmentioning

confidence: 99%

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Electricity Theft Detection in the Presence of Prosumers Using a Cluster-based Multi-feature Detection Model

Alromih¹,

Gope²,

Clark³

2022

Preprint

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Data driven approaches have been widely employed in recent years to detect electricity thefts. Although many techniques have been proposed in the literature, they mainly focus on electricity thefts by consumers of power from the grid. Existing studies do not consider electricity thefts by prosumers, who act as both supplier and consumer in the energy system. This is of great importance as inaccurate reports of prosumers' behaviours can disturb power system operation. Here, the paper examines the role prosumers may play in subverting the energy system and propose a novel means of detecting such malfeasance.Specifically, this work introduces a new electricity theft attack scenarios called balance attacks, where an attacker concurrently modifies his readings along with neighbouring meters in an attempt to balance the total aggregated reading. Such attacks can be difficult to detect by existing solutions that reach detection decisions based on aggregated readings. A novel electricity theft detector is proposed that can detect thefts in the presence of prosumers. Current approaches use either a single model for all users across the system or else a model for each user. Here, a half-way house approach is adopted where a cluster-based detection model is used. In each cluster, the power time series for a user is decomposed into trend, cyclical and residual components. Residual data, along with different features from multiple data sources, are fed in an ML classification algorithm to detect anomalous readings. Simulations have been conducted using a newly generated dataset and results have shown that the proposed model can detect electricity theft with high detection and low error rates. The results also shows that the proposed model can detect thefts with great accuracy from new users.

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Section: A Related Work and Limitationsmentioning

confidence: 96%

Section: A Related Work and Limitationsmentioning

confidence: 99%

Electricity Theft Detection in the Presence of Prosumers Using a Cluster-based Multi-feature Detection Model

Alromih¹,

Gope²,

Clark³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Zhongzo has studied metering data from the advanced metering infrastructure can be used to find abnormal electricity behavior for the detection of electricity theft, which causes huge financial losses to electric companies every year. [1] The metering data are preprocessed, including recover missing and erroneous values and normalization. The classification model based on XGBoost is trained using both benign and malicious samples.…”

Section: Literature Surveymentioning

confidence: 99%

Electricity Theft Detection Using Machine Learning

Dahringer¹

2023

IRJMETS

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The principal source of electrical power loss that has a substantial impact on both the quantity and quality of electrical power is electricity theft. However, the approaches currently in use for detecting this theft-related criminal activity are varied and complex since it is difficult to extract useful information from time-series data due to the uneven nature of the dataset. As energy is so important nowadays, we want to use energy sources wisely, especially when it comes to electricity. In India, power theft has grown to be a serious issue. The generating, transmission, and distribution components make up the power system. Only on the transmission and distribution side is there power theft. Many strategies have been put out to lessen electricity theft. Direct connection to a distribution line, however, is more challenging to investigate. We suggest utilising several machine learning methods to detect electricity theft.

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“…Use Case 2: Detection of Energy Theft in Smart Grid It is estimated that approximately 96 Billion dollars are being lost every year by the utilities due to energy thefts, which lead to increased prices of energy for the consumers [177]. The energy thieves make use of several methods such as tapping a line between a house and the transformer, hacking into meters of neighbours/their own meter and tampering the meters [178]. To minimize electricity thefts, we have to identify the most likely cases of theft that can be investigated further.…”

Section: Xai For Stability Predictionmentioning

confidence: 99%

Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges

Wang¹,

Qureshi²,

Miralles-Pechuaán³

et al. 2021

Preprint

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When 5G began its commercialisation journey around 2020, the discussion on the vision of 6G also surfaced. Researchers expect 6G to have higher bandwidth, coverage, reliability, energy efficiency, lower latency, and, more importantly, an integrated "human-centric" network system powered by artificial intelligence (AI). Such a 6G network will lead to an excessive number of automated decisions made every second. These decisions can range widely, from network resource allocation to collision avoidance for self-driving cars. However, the risk of losing control over decision-making may increase due to high-speed data-intensive AI decision-making beyond designers and users' comprehension. The promising explainable AI (XAI) methods can mitigate such risks by enhancing the transparency of the black box AI decision-making process. This survey paper highlights the need for XAI towards the upcoming 6G age in every aspect, including 6G technologies (e.g., intelligent radio, zero-touch network management) and 6G use cases (e.g., industry 5.0). Moreover, we summarised the lessons learned from the recent attempts and outlined important research challenges in applying XAI for building 6G systems. This research aligns with goals 9, 11, 16, and 17 of the United Nations Sustainable Development Goals (UN-SDG) 1 , promoting innovation and building infrastructure, sustainable and inclusive human settlement, advancing justice and strong institutions, and fostering partnership at the global level.

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Electricity Theft Detection Base on Extreme Gradient Boosting in AMI

Cited by 10 publications

References 13 publications

Electricity Theft Detection in the Presence of Prosumers Using a Cluster-based Multi-feature Detection Model

Electricity Theft Detection in the Presence of Prosumers Using a Cluster-based Multi-feature Detection Model

Electricity Theft Detection Using Machine Learning

Applications of Explainable AI for 6G: Technical Aspects, Use Cases, and Research Challenges

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