Anomaly detection in urban lighting systems using autoencoder and transformer algorithms

The study aims to present the effectiveness of anomaly detection algorithms in lighting systems based on analyzing records from electricity meters. The road lighting management system operates continuously and in real-time, requiring online anomaly detection algorithms. The paper examines two machin...

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Veröffentlicht in:	Scientific reports Jg. 15; H. 1; S. 35972 - 13
Hauptverfasser:	Śmiałkowski, Tomasz, Czyżewski, Andrzej
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 15.10.2025 Nature Publishing Group Nature Portfolio
Schlagworte:	639/4077 639/4077/4073/4071 Accuracy Algorithms Anomaly detection Artificial intelligence Autoencoder Computational efficiency Deep learning Energy consumption Exterior lighting Humanities and Social Sciences Infrastructure Learning algorithms Lighting Lighting systems Machine learning multidisciplinary Neural networks Science Science (multidisciplinary) Smart meters Time series Transformer Urban lighting systems Urban lighting systems Transformer Computational efficiency Autoencoder Anomaly detection
ISSN:	2045-2322, 2045-2322
Online-Zugang:	Volltext
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