Extremely randomised trees machine learning model for electricity theft detection

Akowuah, Emmanuel Kofi; Ikpo, Valentine  Chibueze; Dede, Albert

doi:10.1016/j.mlwa.2023.100458

Cited by 3 publications

(3 citation statements)

References 16 publications

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“…The findings collected show how well the designed detection method works. Author in [16] presented a methodology using randomized tree algorithms to predict electricity theft in smart grids. SMOTE technique is applied to the used dataset to balance the classes, and the hyperparameter of the presented methodology is optimized by applying a grid search optimization approach.…”

Section: A Theft Detection Using Machine Learning Algorithmsmentioning

confidence: 99%

Improving Smart Grids Security: An Active Learning Approach for Smart Grid-Based Energy Theft Detection

Abbas,

Bouazzi,

Ojo

et al. 2024

IEEE Access

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Energy providers and the power grid are severely harmed by electricity theft, which also causes economic and non-technical losses. Energy theft causes a decline in power quality and overall profitability. Smart grids may address the problem of power theft by merging data and energy flow. The analysis of smart grid data helps to find power theft. The prior methods, however, could have done a better job of identifying energy theft. In this research, we presented an active learning-based machine learning model for energy theft identification and classification of a smart grid. The suggested approach is based on the following steps. We use a dataset from the Open Energy Data Initiative (OEDI), an energy research database that gets information from numerous OEDI offices and labs. Next, we preprocess the data and employ machine learning methods like Active Learning (AL) based Random Forests (RFAL), eXtreme Gradient Boosting (XGboostAL), Decision Tree (DTAL), Gradient Boosting (GBAL), K-Nearest Neighbors (KNNAL), Categorical Boosting (CatboostAL) and Light Gradient Boosting Machine (LGBMAL) classifier. Using the smart grid-based energy theft detection dataset, the proposed RFAL model outperforms other competing models and obtains an accuracy of 70.61%. The principles of smart grid tasks streamline decisions and enhance interaction between humans and machines by combining AL with machine learning. The application of this technology in this area has the potential to enhance the accuracy of energy theft detection and electricity-related problems and consequences.

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Section: A Theft Detection Using Machine Learning Algorithmsmentioning

confidence: 99%

Improving Smart Grids Security: An Active Learning Approach for Smart Grid-Based Energy Theft Detection

Abbas,

Bouazzi,

Ojo

et al. 2024

IEEE Access

View full text Add to dashboard Cite

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“…Graphical representation of the CNN-GRU-CS architecture used in the study "Predictive Data Analytics for Electricity Fraud Detection Using Tuned CNN Ensembler in Smart Grid". It is important to emphasize that despite the recent focus on neural networks, efforts have also been made with simpler architectures that have had excellent results, such as the support vector models architectures presented by Petrlik et al (2022) and decision tree architectures presented by Appiah et al (2023) in the study "Extremely randomized trees machine learning model for electricity theft detection." In the latter, the use of the grid search optimization technique to optimize the hyperparameters of the proposed model stands out, as well as the fact of very promising metric yields (98% accuracy, 98% F1, 95% Matthew correlation.…”

Section: Literature Reviewmentioning

confidence: 99%

Electrical Network Fraud Detection Using a CNN+LSTM Model

A. Gomez,

A. Llaugel

2023

IJAEMR

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This work focuses on the persistent problem of energy loss in the Dominican Republic and in Latin America, with an emphasis on non-technical losses caused by electricity fraud. Given the seriousness of the problem, this paper proposes the development and implementation of a fraud classification model in electricity distribution networks using Deep Learning, specifically a combined model of a Convolutional Neural Network Distributed in Time (CNN) and Long Short Term Memory (LSTM). The goal is to understand and evaluate current fraud detection techniques, investigate the applicability and efficiency of CNN + LSTM models in fraud detection, and address potential challenges in implementing this model in Latin America. The justification lies in the considerable financial losses generated by electricity fraud.

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“…The developing nations lose between 20 to 50% of their expected revenue while the developed nations record between 3.5 to 30% loss (Jiang et al, 2014;Musungwini, 2016). Northeast group LLC reported that worldwide, $96 billion are lost due to electricity theft yearly (Appiah et al, 2023). These unfortunate losses in revenue necessitate the need for efficient detection and confirmation techniques (Delgado-Gomes et al, 2015;Xu et al, 2023;Zhao et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Detection and confirmation of electricity thefts in Advanced Metering Infrastructure by Long Short-Term Memory and fuzzy inference system models

Otuoze,

Mustafa,

Sultana

et al. 2024

Nig. J. Technol. Dev.

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The successful implementation of Smart Grids heavily relies on energy efficiency, particularly through the Advanced Metering Infrastructure (AMI) and Smart Electricity Meters (SEM). However, cyber-attacks pose a threat to SEM, with electricity theft being a primary motivation. Despite the valuable data provided by SEM for analytical purposes, existing methods to identify theft involve cumbersome and costly on-site inspections. This research proposes an electricity theft detection model using the Long Short-Term Memory (LSTM) network. The model employs a collective anomaly approach, defining prediction errors through a threshold and forecast horizon. Suspicious consumption profiles are analysed, and a fuzzy inference system (FIS) implemented in MATLAB 2021b is used to model security risks based on these profiles. The study utilizes energy consumption data from four diverse consumer profiles (consumers 1, 2, 3, and 4) to develop consumer-specific LSTM models for detection and an FIS model for confirmation. Tampered consumer data is identified and confirmed based on selected AMI parameters. While all consumers exhibit suspicious profiles at times, only consumers 2 and 3 are confirmed as engaging in electricity theft. This research provides a robust approach to detecting and verifying fraudulent consumption profiles within the context of AMI, offering a more reliable dimension to theft detection and confirmation.

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Extremely randomised trees machine learning model for electricity theft detection

Cited by 3 publications

References 16 publications

Improving Smart Grids Security: An Active Learning Approach for Smart Grid-Based Energy Theft Detection

Improving Smart Grids Security: An Active Learning Approach for Smart Grid-Based Energy Theft Detection

Electrical Network Fraud Detection Using a CNN+LSTM Model

Detection and confirmation of electricity thefts in Advanced Metering Infrastructure by Long Short-Term Memory and fuzzy inference system models

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