Detection of street lighting bulbs information to minimize commercial losses

Soares, Guilherme M.; Almeida, A. G. B.; Mendes, Ricardo Manuel Nogueira; Teixeira, Edmilson Costa; Braga, Henrique A. C.; Machado, Milena; Broetto, R. S.; Castro, M. V. H. B.; Filho, H. O. Gomes; Varejão, Flávio Miguel; Filho, José Gonçalves Pereira; Candeia, A. B.; Sousa, Ricardo J. Alves de

doi:10.1109/icsenst.2013.6727779

Cited by 5 publications

(9 citation statements)

References 3 publications

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“…Part of the research presented in this work has been previously discussed in Soares et al [2][3][4], where the system's hardware and software are detailed, as well as in Broetto et al [9], which analyzed the application of image processing techniques to automatically detect street lighting luminaries, and Broetto and Varejão [10], which combines feature extraction and selection algorithms to optimize system classification accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…This device is designed to determine the type and wattage of lamps installed on poles used for public lighting. Soares et al [2][3][4] have described the main elements of this device, how it operates, and reported test results based on laboratory and field-collected data. These promising results suggest a rapid update of public lighting records, potentially reducing the commercial losses resulting from the lack of energy consumption information.…”

Section: Introductionmentioning

confidence: 99%

“…The collected data is processed by an intelligent algorithm that provides information about the location, type, and power of the lamps. Previous work [2][3][4] yielded relevant results but only a few algorithms and simpler validation methods were used in the experiments. The simpler validation methods may produce over optimistic results.…”

Section: Introductionmentioning

confidence: 99%

“…Figure 1: Vertical cut view of a typical acquisition scene including transportation vehicle, device case and street lighting equipment (extracted from [2]).…”

Section: Introductionmentioning

confidence: 99%

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Analysis and Improvement of Machine Learning Models for Detecting Street Lighting Lamps

Silva,

Salgado,

Varejão

et al. 2023

Learn. Nonlin. Mod.

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The Brazilian public lighting network is maintained by city halls. To bill the energy provided to city halls, energy distribution companies should maintain an updated database of network poles, their lamp types, and wattages. However, it is common to encounter issues with misinformation, where the company is not notified about changes in the public lighting network by city halls and cannot update its database appropriately. To mitigate commercial losses, companies have resorted to sending teams for manual infrastructure checks, which is an expensive, time-consuming, and unreliable process. In this regard, this work aims to optimize the models proposed in the literature capable of accurately classifying the type and wattage of lamps on public lighting poles based on data collected from radiometric sensors and a professional camera. Data is processed using traditional machine learning and deep learning algorithms, along with more sophisticated validation techniques such as data transformation and hyperparameter optimization to achieve improved results. Based on this methodology, the results demonstrate that models employing more robust algorithms (Support Vector Machine, XGBoost, Random Forest, and Multilayer Perceptron) can attain a final average accuracy of 80-86%. This confirms the usefulness of this methodology as an alternative solution to address the issue of public lighting billing.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Figure 1: Vertical cut view of a typical acquisition scene including transportation vehicle, device case and street lighting equipment (extracted from [2]).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analysis and Improvement of Machine Learning Models for Detecting Street Lighting Lamps

Silva,

Salgado,

Varejão

et al. 2023

Learn. Nonlin. Mod.

View full text Add to dashboard Cite

show abstract

“…For example, a research group has proposed the use of a light sensor to gather information of public lighting points in Brazil, this is done to make sure the electricity being used for municipal lighting is correctly reported and payed for [41]. Another study uses forensic investigation procedures to find possible cases of collusion and fraud 370 on meters [42].…”

mentioning

confidence: 99%

Solutions for detection of non-technical losses in the electricity grid: A review

Viegas

Esteves

Melício

et al. 2017

Renewable and Sustainable Energy Reviews

145

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This paper is a review of literature with an analysis on a selection of scientific studies for detection of non-technical losses. Non-technical losses occurring in the electric grid at level of transmission or of distribution have negative impact on economies, affecting utilities, paying consumers and states. The paper is concerned with the lines of research pursued, the main techniques used and the limitations on current solutions. Also, a typology for the categorization of solutions for detection of non-technical losses is proposed and the sources and possible attack/vulnerability points are identified. The selected literature covers a wide range of solutions associated with non-technical losses. Of the 103 selected studies, 6 are theoretical, 25 propose hardware solutions and 72 propose non-hardware solutions. Data based classification models and data from consumption with high resolution are respectively required in about 47% and 35% of the reported solutions. Available solutions cover a wide range of cases, with the main limitation found being the lack of an unified solution, which enables the detection of all kinds of non-technical losses.

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On the Use of Light Sensors and Pattern Recognition Techniques for Automated Detection of Street Lighting Lamps

Soares

Almeida

Mendes

et al. 2015

Smart Sensors, Measurement and Instrumentation

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Detection of street lighting bulbs information to minimize commercial losses

Cited by 5 publications

References 3 publications

Analysis and Improvement of Machine Learning Models for Detecting Street Lighting Lamps

Analysis and Improvement of Machine Learning Models for Detecting Street Lighting Lamps

Solutions for detection of non-technical losses in the electricity grid: A review

On the Use of Light Sensors and Pattern Recognition Techniques for Automated Detection of Street Lighting Lamps

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