Employing Gradient Boosting and Anomaly Detection for Prediction of Frauds in Energy Consumption

Albiero, Beatriz; Santos, Ricardo de Araújo; Uyrá, Estevo; Vilarino, Ramon; Silva, Juliano Domingues da; Souza, Tales F. B.; Vicente, Renato; Yamouni, Sami

doi:10.5753/eniac.2019.9345

Cited by 3 publications

(2 citation statements)

References 18 publications

(24 reference statements)

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“…In the same manner, in [116], a grid search is deployed to capture the best parameter configuration of a GBM based anomaly detection. While in [117], the authors predict energy frauds though the identification of power consumption anomalies using a GBM based scheme. In [118], a GTB based anomaly detection is investigated along with other data mining techniques using power consumption pricing data.…”

Section: Ensemble Methods (E)mentioning

confidence: 99%

“…To that end, detecting non-technical-loss and electricity theft has been introduced as an information technology related challenge, which requires novel methods based on artificial intelligence, data mining and forecasting [102,107]. Moreover, separating between behavioral consumption anomalies, frauds and unintentional consumption deviations is reported as a current research trend to provide an accurate feedback to end-users and energy providers [117,147].…”

Section: Applicationsmentioning

confidence: 99%

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Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives

Himeur,

Ghanem,

Alsalemi

et al. 2020

Preprint

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Enormous amounts of data are being produced everyday by submeters and smart sensors installed in different kinds of buildings. If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly. Therefore, anomaly detection could stop a minor problem to become widespread, costly and time-consuming issue. Moreover, this will help in better decision-making to reduce wasted energy and promote sustainable and energy efficiency behavior. In this regard, this paper is proposed to indepthly review existing frameworks of anomaly detection in power consumption and provide a critical analysis of existing solutions. Specifically, a comprehensive survey is introduced, in which a novel taxonomy is introduced to classify existing algorithms based on different factors adopted in their implementation, such as the machine learning algorithm, feature extraction approach, detection level, computing platform, application scenario and privacy preservation. To the best of the authors' knowledge, this is the first review article that discusses the anomaly detection in building energy consumption. Moving forward, important findings along with domain-specific problems, difficulties and challenges that remain unresolved are thoroughly discussed, including the absence of: (i) precise definitions of anomalous power consumptions, (ii) annotated datasets, (iii) unified metrics to assess the performance of existing solutions, and (iv) platforms for reproducibility. Following, insights about current research trends that anomaly detection technology needs to target for widespreading its application and facilitate its implementation are described before deriving a set of challenging future directions attracting significant research and development attention.

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Section: Ensemble Methods (E)mentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives

Himeur,

Ghanem,

Alsalemi

et al. 2020

Preprint

View full text Add to dashboard Cite

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Trimming outliers using trees

Arjunan

Miller

2022

Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

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Machine Learning based Smart Electricity Monitoring & Fault Detection for Smart City 4.0 Ecosystem

Mondal,

Banerjee,

Ghosh

et al. 2023

Advances in Computing Communications and Informatics

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Growing electricity needs among the vast majority of the population seconded by a voluminous increase in electrical appliances have led to a huge surge in electric power demands. With thediminishing unit price of electric meters and increase of loading, it has been observed that a certain amount of electric meters generate faulty readings after exhaustive usage. This results in erroneous meter readings thereby affecting the billings. We propose a fault detecting learning algorithm that is trained by early meter readings and compares the actual meter reading (AMR) with the predicted meter reading (PMR). The decision matrix generates an alarm if |PMR-AMR|>T; where T equals the threshold limit. T itself is decided by the learning algorithm depending upon the meter variance. Moreover, our system also detects if there is any power theft as such an action would result in a sudden rise in AMR. The learning algorithm deploys six binary classifiers which reflect an accuracy of 98.24% for the detection module and an error rate of 1.26% for the prediction module.

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Employing Gradient Boosting and Anomaly Detection for Prediction of Frauds in Energy Consumption

Cited by 3 publications

References 18 publications

Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives

Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives

Trimming outliers using trees

Machine Learning based Smart Electricity Monitoring & Fault Detection for Smart City 4.0 Ecosystem

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