Detection of Anomalies in the Operation of a Road Lighting System Based on Data from Smart Electricity Meters
Smart meters in road lighting systems create new opportunities for automatic diagnostics of undesirable phenomena such as lamp failures, schedule deviations, or energy theft from the power grid. Such a solution fits into the smart cities concept, where an adaptive lighting system creates new challen...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-12-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/24/9438 |
| _version_ | 1797459655718338560 |
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| author | Tomasz Śmiałkowski Andrzej Czyżewski |
| author_facet | Tomasz Śmiałkowski Andrzej Czyżewski |
| author_sort | Tomasz Śmiałkowski |
| collection | DOAJ |
| description | Smart meters in road lighting systems create new opportunities for automatic diagnostics of undesirable phenomena such as lamp failures, schedule deviations, or energy theft from the power grid. Such a solution fits into the smart cities concept, where an adaptive lighting system creates new challenges with respect to the monitoring function. This article presents research results indicating the practical feasibility of real-time detection of anomalies in a road lighting system based on analysis of data from smart energy meters. Short-term time series forecasting was used first. In addition, two machine learning methods were used: one based on an autoregressive integrating moving average periodic model (SARIMA) and the other based on a recurrent network (RNN) using long short-term memory (LSTM). The algorithms were tested on real data from an extensive lighting system installation. Both approaches enable the creation of self-learning, real-time anomaly detection algorithms. Therefore, it is possible to implement them on edge computing layer devices. A comparison of the algorithms indicated the advantage of the method based on the SARIMA model. |
| first_indexed | 2024-03-09T16:54:31Z |
| format | Article |
| id | doaj.art-2b9dcc0d31cf416dbe20f57f9e5db183 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T16:54:31Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-2b9dcc0d31cf416dbe20f57f9e5db1832023-11-24T14:36:54ZengMDPI AGEnergies1996-10732022-12-011524943810.3390/en15249438Detection of Anomalies in the Operation of a Road Lighting System Based on Data from Smart Electricity MetersTomasz Śmiałkowski0Andrzej Czyżewski1TSTRONIC sp. z.o.o., 83-011 Gdansk, PolandFaculty of Electronics, Telecommunication and Informatics, Gdansk University of Technology, 80-233 Gdansk, PolandSmart meters in road lighting systems create new opportunities for automatic diagnostics of undesirable phenomena such as lamp failures, schedule deviations, or energy theft from the power grid. Such a solution fits into the smart cities concept, where an adaptive lighting system creates new challenges with respect to the monitoring function. This article presents research results indicating the practical feasibility of real-time detection of anomalies in a road lighting system based on analysis of data from smart energy meters. Short-term time series forecasting was used first. In addition, two machine learning methods were used: one based on an autoregressive integrating moving average periodic model (SARIMA) and the other based on a recurrent network (RNN) using long short-term memory (LSTM). The algorithms were tested on real data from an extensive lighting system installation. Both approaches enable the creation of self-learning, real-time anomaly detection algorithms. Therefore, it is possible to implement them on edge computing layer devices. A comparison of the algorithms indicated the advantage of the method based on the SARIMA model.https://www.mdpi.com/1996-1073/15/24/9438road lighting systemanomaly detectionmachine learningsmart citysmart metersSARIMA |
| spellingShingle | Tomasz Śmiałkowski Andrzej Czyżewski Detection of Anomalies in the Operation of a Road Lighting System Based on Data from Smart Electricity Meters Energies road lighting system anomaly detection machine learning smart city smart meters SARIMA |
| title | Detection of Anomalies in the Operation of a Road Lighting System Based on Data from Smart Electricity Meters |
| title_full | Detection of Anomalies in the Operation of a Road Lighting System Based on Data from Smart Electricity Meters |
| title_fullStr | Detection of Anomalies in the Operation of a Road Lighting System Based on Data from Smart Electricity Meters |
| title_full_unstemmed | Detection of Anomalies in the Operation of a Road Lighting System Based on Data from Smart Electricity Meters |
| title_short | Detection of Anomalies in the Operation of a Road Lighting System Based on Data from Smart Electricity Meters |
| title_sort | detection of anomalies in the operation of a road lighting system based on data from smart electricity meters |
| topic | road lighting system anomaly detection machine learning smart city smart meters SARIMA |
| url | https://www.mdpi.com/1996-1073/15/24/9438 |
| work_keys_str_mv | AT tomaszsmiałkowski detectionofanomaliesintheoperationofaroadlightingsystembasedondatafromsmartelectricitymeters AT andrzejczyzewski detectionofanomaliesintheoperationofaroadlightingsystembasedondatafromsmartelectricitymeters |