Summary of A Real-time Anomaly Detection Using Convolutional Autoencoder with Dynamic Threshold, by Sarit Maitra et al.
A Real-time Anomaly Detection Using Convolutional Autoencoder with Dynamic Threshold
by Sarit Maitra, Sukanya Kundu, Aishwarya Shankar
First submitted to arxiv on: 5 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed hybrid modeling approach combines statistics and a Convolutional Autoencoder with a dynamic threshold to detect anomalies in real-time smart metering systems, ensuring reliable estimates of energy consumption evolution. The dynamic threshold is determined based on Mahalanobis distance and moving averages, allowing for the identification of unusual data movements and early warning signals. Tested using real-life energy consumption data, this solution includes a real-time anomaly detection system that connects to an advanced monitoring platform, contributing significantly to the detection of anomalies and prevention of disasters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new approach is being developed to help smart meters track energy usage more accurately. This method uses a combination of statistical techniques and machine learning to detect unusual patterns in energy consumption data. The goal is to identify when something is wrong with the meter’s readings, so it can be fixed before it causes problems or waste money. The method has been tested using real-world data and shows promise for improving the reliability of energy usage tracking. |
Keywords
* Artificial intelligence * Anomaly detection * Autoencoder * Machine learning * Tracking
