A Data Mining-Based Dynamical Anomaly Detection Method for Integrating with an Advance Metering System

• URL: http://arxiv.org/abs/2405.02574v1
• Date: Sat, 4 May 2024 05:26:13 GMT
• Title: A Data Mining-Based Dynamical Anomaly Detection Method for Integrating with an Advance Metering System
• Authors: Sarit Maitra,
• Abstract summary: Building operations consume 30% of total power consumption and contribute 26% of global power-related emissions. This work investigates both supervised and unsupervised approaches to detect anomalies at the meter level. This system is designed to provide real-time detection of anomalies at the meter level.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Building operations consume 30% of total power consumption and contribute 26% of global power-related emissions. Therefore, monitoring, and early detection of anomalies at the meter level are essential for residential and commercial buildings. This work investigates both supervised and unsupervised approaches and introduces a dynamic anomaly detection system. The system introduces a supervised Light Gradient Boosting machine and an unsupervised autoencoder with a dynamic threshold. This system is designed to provide real-time detection of anomalies at the meter level. The proposed dynamical system comes with a dynamic threshold based on the Mahalanobis distance and moving averages. This approach allows the system to adapt to changes in the data distribution over time. The effectiveness of the proposed system is evaluated using real-life power consumption data collected from smart metering systems. This empirical testing ensures that the system's performance is validated under real-world conditions. By detecting unusual data movements and providing early warnings, the proposed system contributes significantly to visual analytics and decision science. Early detection of anomalies enables timely troubleshooting, preventing financial losses and potential disasters such as fire incidents.

Related papers

• A Real-time Anomaly Detection Using Convolutional Autoencoder with Dynamic Threshold [0.0]
  This work introduces a hybrid modeling approach combining statistics and a Convolutional Autoencoder with a dynamic threshold. The solution includes a real-time, meter-level anomaly detection system that connects to an advanced monitoring system.
  arXiv  Detail & Related papers  (2024-04-05T11:03:36Z)
• Detecting and Mitigating System-Level Anomalies of Vision-Based Controllers [7.095058159492494]
  Vision-based controllers can make erroneous predictions when faced with novel or out-of-distribution inputs. In this work, we introduce a run-time anomaly monitor to detect and mitigate such closed-loop, system-level failures. We validate the proposed approach on an autonomous aircraft taxiing system that uses a vision-based controller for taxiing.
  arXiv  Detail & Related papers  (2023-09-23T20:33:38Z)
• Interactive System-wise Anomaly Detection [66.3766756452743]
  Anomaly detection plays a fundamental role in various applications. It is challenging for existing methods to handle the scenarios where the instances are systems whose characteristics are not readily observed as data. We develop an end-to-end approach which includes an encoder-decoder module that learns system embeddings.
  arXiv  Detail & Related papers  (2023-04-21T02:20:24Z)
• A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics [56.86150790999639]
  We present two anomaly detection and classification approaches, namely the Matrix Profile algorithm and anomaly transformer. The Matrix Profile algorithm is shown to be well suited as a generalizable approach for detecting real-time anomalies in streaming time-series data. A series of custom filters is created and added to the detector to tune its sensitivity, recall, and detection accuracy.
  arXiv  Detail & Related papers  (2022-09-23T06:09:35Z)
• Ranking-Based Physics-Informed Line Failure Detection in Power Grids [66.0797334582536]
  Real-time and accurate detecting of potential line failures is the first step to mitigating the extreme weather impact and activating emergency controls. Power balance equations nonlinearity, increased uncertainty in generation during extreme events, and lack of grid observability compromise the efficiency of traditional data-driven failure detection methods. This paper proposes a Physics-InformEd Line failure Detector (FIELD) that leverages grid topology information to reduce sample and time complexities and improve localization accuracy.
  arXiv  Detail & Related papers  (2022-08-31T18:19:25Z)
• Smart Meter Data Anomaly Detection using Variational Recurrent Autoencoders with Attention [0.0]
  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.
  arXiv  Detail & Related papers  (2022-06-08T19:39:51Z)
• Cloud Failure Prediction with Hierarchical Temporary Memory: An Empirical Assessment [64.73243241568555]
  Hierarchical Temporary Memory (HTM) is an unsupervised learning algorithm inspired by the features of the neocortex. This paper presents the first systematic study that assesses HTM in the context of failure prediction.
  arXiv  Detail & Related papers  (2021-10-06T07:09:45Z)
• Data-driven Residual Generation for Early Fault Detection with Limited Data [4.129225533930966]
  In many complex systems it is not feasible to develop highly accurate models for the systems. Data-driven solutions have received an immense attention in the industries systems for several practical reasons. Unlike the model-based methods it is straight forward to combine time series measurements such as pressure and voltage with other sources of information.
  arXiv  Detail & Related papers  (2021-09-28T03:18:03Z)
• No Need to Know Physics: Resilience of Process-based Model-free Anomaly Detection for Industrial Control Systems [95.54151664013011]
  We present a novel framework to generate adversarial spoofing signals that violate physical properties of the system. We analyze four anomaly detectors published at top security conferences.
  arXiv  Detail & Related papers  (2020-12-07T11:02:44Z)
• Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems [65.44033635330604]
  We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system. We show that its use of intrinsic linear dynamics offers a number of desirable properties in terms of interpretability and parsimony. Results are presented for a test case using load data from an electrical grid.
  arXiv  Detail & Related papers  (2020-10-08T20:25:52Z)
• Monitoring and Diagnosability of Perception Systems [21.25149064251918]
  Perception is a critical component of high-integrity applications of robotics and autonomous systems, such as self-driving cars. Despite the paramount importance of perception systems, there is no formal approach for system-level monitoring. We propose a mathematical model for runtime monitoring and fault detection of perception systems.
  arXiv  Detail & Related papers  (2020-05-24T18:09:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.