A Real-time Anomaly Detection Using Convolutional Autoencoder with Dynamic Threshold
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929304033558528 |
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| author | Maitra, Sarit Kundu, Sukanya Shankar, Aishwarya |
| author_facet | Maitra, Sarit Kundu, Sukanya Shankar, Aishwarya |
| contents | The majority of modern consumer-level energy is generated by real-time smart metering systems. These frequently contain anomalies, which prevent reliable estimates of the series' evolution. This work introduces a hybrid modeling approach combining statistics and a Convolutional Autoencoder with a dynamic threshold. The threshold is determined based on Mahalanobis distance and moving averages. It has been tested using real-life energy consumption data collected from smart metering systems. The solution includes a real-time, meter-level anomaly detection system that connects to an advanced monitoring system. This makes a substantial contribution by detecting unusual data movements and delivering an early warning. Early detection and subsequent troubleshooting can financially benefit organizations and consumers and prevent disasters from occurring. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_04311 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Real-time Anomaly Detection Using Convolutional Autoencoder with Dynamic Threshold Maitra, Sarit Kundu, Sukanya Shankar, Aishwarya Machine Learning Artificial Intelligence The majority of modern consumer-level energy is generated by real-time smart metering systems. These frequently contain anomalies, which prevent reliable estimates of the series' evolution. This work introduces a hybrid modeling approach combining statistics and a Convolutional Autoencoder with a dynamic threshold. The threshold is determined based on Mahalanobis distance and moving averages. It has been tested using real-life energy consumption data collected from smart metering systems. The solution includes a real-time, meter-level anomaly detection system that connects to an advanced monitoring system. This makes a substantial contribution by detecting unusual data movements and delivering an early warning. Early detection and subsequent troubleshooting can financially benefit organizations and consumers and prevent disasters from occurring. |
| title | A Real-time Anomaly Detection Using Convolutional Autoencoder with Dynamic Threshold |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2404.04311 |