Smart Monitoring of Electrical Circuits for Distinction of Connected Devices through Current Pattern Analysis using Machine Learning Algorithms

Smart Monitoring of Electrical Circuits for Distinction of Connected Devices through Current Pattern Analysis using Machine Learning Algorithms

Energy Monitoring is a crucial activity in Energy Efficiency, which involves the study of techniques to supervise the energy consumption in a power grid, regarding the main purpose is to assure a good level of detail, to achieve consumption quotas for each connected device, for a low infrastructure...

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Bibliographic Details
Main Authors: Jean Phelipe de Oliveira Lima, Carlos Maurício Seródio Figueiredo
Format: Article
Language:English
Published: Asociación Española para la Inteligencia Artificial 2020-09-01
Series:Inteligencia Artificial
Subjects:
Applications of AI
Pattern Recognition
Electric Current Pattern Analysis
Machine Learning
Energy Efficiency
Online Access:https://journal.iberamia.org/index.php/intartif/article/view/487
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spelling doaj-cb616c27a5c5429c88c9803e2ff466b22021-02-19T16:38:14ZengAsociación Española para la Inteligencia ArtificialInteligencia Artificial1137-36011988-30642020-09-01236610.4114/intartif.vol23iss66pp36-50Smart Monitoring of Electrical Circuits for Distinction of Connected Devices through Current Pattern Analysis using Machine Learning AlgorithmsJean Phelipe de Oliveira Lima 0Carlos Maurício Seródio Figueiredo1 Universidade do Estado do Amazonas, Manaus-AM, Brazil Universidade do Estado do Amazonas, Manaus-AM, Brazil Energy Monitoring is a crucial activity in Energy Efficiency, which involves the study of techniques to supervise the energy consumption in a power grid, regarding the main purpose is to assure a good level of detail, to achieve consumption quotas for each connected device, for a low infrastructure cost. This paper presents the evaluation of different Machine Learning models to classify electric current patterns to identify and monitor electric charges present in circuits with a single sensing device. The models were trained and validated by a database created from signal samples of 4 electrical devices: Notebook Charger, Refrigerator, Blender and Fan. The models that presented the best metrics achieved, respectively, 97% and 100% Accuracy and 98% and 100% F1-Score, surpassing results obtained in related researches. https://journal.iberamia.org/index.php/intartif/article/view/487Applications of AIPattern RecognitionElectric Current Pattern AnalysisMachine LearningEnergy Efficiency
collection DOAJ
language English
format Article
sources DOAJ
author Jean Phelipe de Oliveira Lima
Carlos Maurício Seródio Figueiredo
spellingShingle Jean Phelipe de Oliveira Lima
Carlos Maurício Seródio Figueiredo
Smart Monitoring of Electrical Circuits for Distinction of Connected Devices through Current Pattern Analysis using Machine Learning Algorithms
Inteligencia Artificial
Applications of AI
Pattern Recognition
Electric Current Pattern Analysis
Machine Learning
Energy Efficiency
author_facet Jean Phelipe de Oliveira Lima
Carlos Maurício Seródio Figueiredo
author_sort Jean Phelipe de Oliveira Lima
title Smart Monitoring of Electrical Circuits for Distinction of Connected Devices through Current Pattern Analysis using Machine Learning Algorithms
title_short Smart Monitoring of Electrical Circuits for Distinction of Connected Devices through Current Pattern Analysis using Machine Learning Algorithms
title_full Smart Monitoring of Electrical Circuits for Distinction of Connected Devices through Current Pattern Analysis using Machine Learning Algorithms
title_fullStr Smart Monitoring of Electrical Circuits for Distinction of Connected Devices through Current Pattern Analysis using Machine Learning Algorithms
title_full_unstemmed Smart Monitoring of Electrical Circuits for Distinction of Connected Devices through Current Pattern Analysis using Machine Learning Algorithms
title_sort smart monitoring of electrical circuits for distinction of connected devices through current pattern analysis using machine learning algorithms
publisher Asociación Española para la Inteligencia Artificial
series Inteligencia Artificial
issn 1137-3601
1988-3064
publishDate 2020-09-01
description Energy Monitoring is a crucial activity in Energy Efficiency, which involves the study of techniques to supervise the energy consumption in a power grid, regarding the main purpose is to assure a good level of detail, to achieve consumption quotas for each connected device, for a low infrastructure cost. This paper presents the evaluation of different Machine Learning models to classify electric current patterns to identify and monitor electric charges present in circuits with a single sensing device. The models were trained and validated by a database created from signal samples of 4 electrical devices: Notebook Charger, Refrigerator, Blender and Fan. The models that presented the best metrics achieved, respectively, 97% and 100% Accuracy and 98% and 100% F1-Score, surpassing results obtained in related researches.
topic Applications of AI
Pattern Recognition
Electric Current Pattern Analysis
Machine Learning
Energy Efficiency
url https://journal.iberamia.org/index.php/intartif/article/view/487
work_keys_str_mv AT jeanphelipedeoliveiralima smartmonitoringofelectricalcircuitsfordistinctionofconnecteddevicesthroughcurrentpatternanalysisusingmachinelearningalgorithms
AT carlosmauricioserodiofigueiredo smartmonitoringofelectricalcircuitsfordistinctionofconnecteddevicesthroughcurrentpatternanalysisusingmachinelearningalgorithms
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