Detection of Anomalies in the Operation of a Road Lighting System Based on Data from Smart Electricity Meters

Śmiałkowski, Tomasz; Czyżewski, Andrzej

doi:10.3390/en15249438

Cited by 3 publications

(2 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The current research on checkpoint energy meters is relatively limited. Sun et al [3] propose a method for identifying abnormal measurement data of checkpoint energy meters based on pseudo-anomalous point identification, which can effectively identify abnormal data. During the operation of the checkpoint meter, they will cause certain measurement errors.…”

Section: Introductionmentioning

confidence: 99%

A Remote Out-of-Tolerance Classification Method of Gateway Electric Energy Meters Based on Feature Selection and Deep Belief Network

Li,

Wang,

et al. 2024

Frontiers in Artificial Intelligence and Applications

View full text Add to dashboard Cite

Accurate identification of the out-of-tolerance of the gateway electric energy meter plays a vital role in ensuring the stability of the new power system and maintaining the fairness of the transaction. As a key metering device in power generation enterprises, network connections between power grids, inter-provincial network connections, and other metering devices, the importance of checkpoint energy measurement is self-evident. Therefore, studying the causes of metering errors and the classification of outlier checkpoint metering devices is significant. A remote out-of-tolerance gateway electric energy meters classification method based on feature selection and deep belief network is proposed. This paper first uses Spearman and Pearson correlation coefficients to select the main influencing factors of checkpoint energy metering error and then trains a deep belief network model to complete the detection of outlier checkpoint energy metering. Finally, experimental results verify the superiority of the proposed method.

show abstract

Section: Introductionmentioning

confidence: 99%

A Remote Out-of-Tolerance Classification Method of Gateway Electric Energy Meters Based on Feature Selection and Deep Belief Network

Li,

Wang,

et al. 2024

Frontiers in Artificial Intelligence and Applications

View full text Add to dashboard Cite

show abstract

“…There are many methods for detecting anomalies in time series: statistical methods such as ARIMA/SARIMA, machine learning methods such as K-Means Clustering, Isolation Forest, or methods using neural networks (Deep Learning), among others: Convolutional Neural Networks, Long Short Term Memory (LSTM), Autoencoder. In the paper [10], the authors analyzed statistical methods based on the SARIMA model and Deep Learning methods based on LSTM networks. Detecting anomalies in energy consumption from smart meters is used in fraud detection systems (FDS) for advanced metering infrastructure.…”

Section: Introductionmentioning

confidence: 99%

Autoencoder Application for Anomaly Detection in Power Consumption of Lighting Systems

Śmiałkowski,

Czyżewski

2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

Detecting energy consumption anomalies is a popular topic of industrial research, but there is a noticeable lack of research reported in the literature on energy consumption anomalies for road lighting systems. However, there is a need for such research because the lighting system, a key element of the Smart City concept, creates new monitoring opportunities and challenges. This paper examines algorithms based on the deep learning method using the Autoencoder model with LSTM and 1D Convolutional networks for various configurations and training periods. The evaluation of the algorithms was carried out based on real data from an extensive lighting control system. A practical approach was proposed using real-time, unsupervised algorithms employing limited computing resources that can be implemented in industrial devices designed to control intelligent city lighting. An anomaly detection algorithm based on classic LSTM networks, single-layer and multi-layer, was used for comparison purposes. Error matrix calculus was used to assess the quality of the models. It was shown that based on the Autoencoder method, it is possible to construct an algorithm that correctly detects anomalies in power measurements of lighting systems, and it is possible to build a model so that the algorithm works correctly regardless of the season of the year.

show abstract

PRISMA on Machine Learning Techniques in Smart City Development

Ionescu,

Jula,

Hurduzeu

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

This article investigates the innovative role of machine learning (ML) in the development of smart cities, emphasizing the critical interrelationship between ML and urban environments. While existing studies address ML and urban settings separately, this work uniquely examines their intersection, highlighting the transformative potential of ML in urban development. Utilizing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, a systematic and reproducible approach was employed to review 42 relevant studies. The analysis reveals four key themes: transportation and traffic optimization, people and event flow tracking, sustainability applications, and security use cases. These findings underscore ML’s ability to revolutionize smart city initiatives by enhancing efficiency, sustainability, and security. This review identifies significant research gaps and proposes future directions, positioning ML as a cornerstone in the evolution of intelligent urban environments.

show abstract

Detection of Anomalies in the Operation of a Road Lighting System Based on Data from Smart Electricity Meters

Cited by 3 publications

References 33 publications

A Remote Out-of-Tolerance Classification Method of Gateway Electric Energy Meters Based on Feature Selection and Deep Belief Network

A Remote Out-of-Tolerance Classification Method of Gateway Electric Energy Meters Based on Feature Selection and Deep Belief Network

Autoencoder Application for Anomaly Detection in Power Consumption of Lighting Systems

PRISMA on Machine Learning Techniques in Smart City Development

Contact Info

Product

Resources

About