Summary of A Data Mining-based Dynamical Anomaly Detection Method For Integrating with An Advance Metering System, by Sarit Maitra
A Data Mining-Based Dynamical Anomaly Detection Method for Integrating with an Advance Metering System
by Sarit Maitra
First submitted to arxiv on: 4 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The dynamic anomaly detection system introduced in this paper aims to monitor and detect anomalies at the meter level for residential and commercial buildings, which consume 30% of total power consumption and contribute 26% of global power-related emissions. The approach combines supervised (Light Gradient Boosting) and unsupervised (autoencoder with dynamic threshold) methods to provide real-time detection. A dynamic threshold is calculated using Mahalanobis distance and moving averages to adapt to changes in data distribution over time. The system’s performance is evaluated using real-life power consumption data from smart metering systems, ensuring validation under real-world conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to detect unusual patterns in energy usage, which helps prevent problems like fires or financial losses. It uses two different methods (one based on machine learning and the other based on self-organization) to find anomalies in data from smart meters. The system can adapt to changing patterns over time, making it useful for real-world applications. |
Keywords
» Artificial intelligence » Anomaly detection » Autoencoder » Boosting » Machine learning » Supervised » Unsupervised
