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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Bibliographic Details
Published in:Scientific reports Vol. 15; no. 1; pp. 35972 - 13
Main Authors: Śmiałkowski, Tomasz, Czyżewski, Andrzej
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 15.10.2025
Nature Publishing Group
Nature Portfolio
Subjects:
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 Access:Get full text
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Summary: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 machine learning-based algorithms: Autoencoder with LSTM-type recurrent neural network and Transformer. The results obtained for these algorithms are compared with a simple mechanism for comparing energy consumption in consecutive periods. Classification metrics such as error matrix, sensitivity, precision, and F1-score were used to evaluate the performance of the algorithms. The analysis showed that the Autoencoder algorithm achieves better accuracy (F1-score = 0.9565) and requires significantly fewer computing resources than the Transformer algorithm. Although less efficient (F1-score = 0.8125), the Transformer algorithm also demonstrates the ability to detect anomalies in the road lighting system effectively. Implementing the Autoencoder algorithm on an actual ILED platform allows anomaly detection with a delay of 15 min, which is sufficient to take corrective action. The conclusions of this study indicate the significant advantage of machine learning-based algorithms in anomaly detection in lighting systems, which can significantly improve the reliability and efficiency of urban lighting management.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-19414-8