A Novel Approach for Anomaly Detection in Power Consumption Data

Chahla, C.; Snoussi, Hichem; Merghem, Leïla; Esseghir, Moez

doi:10.5220/0007361704830490

Cited by 18 publications

(15 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, after the assembly process of a connector is finished, it is followed by the inspection process, there is a point at which one of the assembled part dimensions is measured with a value far from the rest of the measurements and even exceeds the design specification limits. This abnormal behavior is known as point anomaly [6], [8]. Samples of point anomaly in 2-dimensional space are illustrated in Fig.…”

Section: A Point Anomalymentioning

confidence: 99%

“…Contextual anomaly also referred to as conditional anomaly, is a data instance that is considered as an anomaly in a specific context, but not otherwise [6]. This type of anomaly is common in time-series data streams, for example, after the assembly process of a connector is finished, the inspection system reports dimensions measurements for the assembled part with very high or very low values, but this happens only when the assembly machine starts working, or before it stops, or during high load hours, all these are considered as contextual anomalous behavior in the assembly machine.…”

Section: B Contextual Anomalymentioning

confidence: 99%

“…Sample of contextual anomaly is illustrated in Fig. 1 (N2 data points) with a condition that these data points hold different context from N1 data points [6], [8].…”

Section: B Contextual Anomalymentioning

confidence: 99%

“…Collective anomaly is a collection of individual data points, wherein each of the individual points in isolation appears as normal data instance while observed in a group that shows unusual characteristics with respect to an entire data set [6]. Collective anomaly in the assembly process appears when a sequence of assembled parts is reported with dimension's measurements that deviated from the normal behavior by the inspection system [6], [8]. Samples of collective anomaly is illustrated in Fig.…”

Section: Collective Anomalymentioning

confidence: 99%

“…Anomaly detection has been used in a wide range of application domains; for example, credit card fraud detection, insurance, health care, computer security intrusion detection, image processing, security-critical device failure detection and many more [4], [6].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Assembly Line Anomaly Detection and Root Cause Analysis Using Machine Learning

Abdelrahman

Keikhosrokiani

2020

IEEE Access

View full text Add to dashboard Cite

Anomaly detection is becoming widely used in Manufacturing Industry to enhance product quality. At the same time, it plays a great role in several other domains due to the fact that anomaly may reveal rare but represent an important phenomenon. The objective of this paper is to detect anomalies and identify the possible variables that caused these anomalies on historical assembly data for two series of products. Multiple anomaly detection techniques were performed; HBOS, IForest, KNN, CBLOF, OCSVM, LOF, and ABOD. Moreover, we used AUROC and Rank Power as performance metrics, followed by Boosting ensemble learning method to ensure the best anomaly detectors robustness. The techniques that gave the highest performance are KNN, ABOD for both product series datasets with 0.95 and 0.99 AUROC respectively. Finally, we applied a statistical root cause analysis on the detected anomalies with the use of Pareto chart to visualize the frequency of the possible causes and its cumulative occurrence. The results showed that there are seven rejection causes for both product series, whereas the first three causes are responsible for 85% of the rejection rates. Besides, assembly machines engineers reported a significant reduction in the rejection rates in both assembly machines after tuning the specification limits of the rejection causes identified by this research results.

show abstract

Section: A Point Anomalymentioning

confidence: 99%

Section: B Contextual Anomalymentioning

confidence: 99%

“…Sample of contextual anomaly is illustrated in Fig. 1 (N2 data points) with a condition that these data points hold different context from N1 data points [6], [8].…”

Section: B Contextual Anomalymentioning

confidence: 99%

Section: Collective Anomalymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Assembly Line Anomaly Detection and Root Cause Analysis Using Machine Learning

Abdelrahman

Keikhosrokiani

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

Gradient boosting and Shapley additive explanations for fraud detection in electricity distribution grids

Santos¹,

Yamouni²,

Albiero³

et al. 2021

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Fraud in electrical energy consumption represents a critical economic burden for utility companies around the world. Despite systematic efforts to mitigate electricity theft, this practice persists mostly in developing countries where companies rely on traditional detection methods. In Brazil it is estimated that around 7% of the total electrical energy available for consumption in 2016 was lost due to frauds. Here we describe an efficient and scalable system to predict fraudulent behavior and guide in loco inspections. We compared the performances of several machine learning algorithms using consumption and inspection data provided by CPFL Energia. We show that proper feature engineering and boosted classification trees trained with XGBoost are able to extract patterns related to fraud occurrence and to achieve predictive power of practical consequences. Moreover, we demonstrate how Shapley additive explanation (SHAP) values can be employed to build user friendly explanations. Together, the proposed model and its explainers contribute not only to reveal potentially fraudulent behavior but also to understand root causes, what can be used to devise robust mitigation strategies.

show abstract

Smart Meter Data Anomaly Detection Using Variational Recurrent Autoencoders with Attention

Dai

Liu

Heller³

et al. 2022

Communications in Computer and Information Science

View full text Add to dashboard Cite

In the digitization of energy systems, sensors and smart meters are increasingly being used to monitor production, operation and demand. Detection of anomalies based on smart meter data is crucial to identify potential risks and unusual events at an early stage, which can serve as a reference for timely initiation of appropriate actions and improving management. However, smart meter data from energy systems often lack labels and contain noise and various patterns without distinctively cyclical. Meanwhile, the vague definition of anomalies in different energy scenarios and highly complex temporal correlations pose a great challenge for anomaly detection. Many traditional unsupervised anomaly detection algorithms such as cluster-based or distance-based models are not robust to noise and not fully exploit the temporal dependency in a time series as well as other dependencies amongst multiple variables (sensors). This paper proposes an unsupervised anomaly detection method based on a Variational Recurrent Autoencoder with attention mechanism. with "dirty" data from smart meters, our method pre-detects missing values and global anomalies to shrink their contribution while training. This paper makes a quantitative comparison with the VAE-based baseline approach and four other unsupervised learning methods, demonstrating its effectiveness and superiority. This paper further validates the proposed method by a real case study of detecting the anomalies of water supply temperature from an industrial heating plant.

show abstract

A Novel Approach for Anomaly Detection in Power Consumption Data

Cited by 18 publications

References 0 publications

Assembly Line Anomaly Detection and Root Cause Analysis Using Machine Learning

Assembly Line Anomaly Detection and Root Cause Analysis Using Machine Learning

Gradient boosting and Shapley additive explanations for fraud detection in electricity distribution grids

Smart Meter Data Anomaly Detection Using Variational Recurrent Autoencoders with Attention

Contact Info

Product

Resources

About