Use of Binary Classification in Non-Invasive Load Monitoring
The increasing energy intensity of the economy has led us to look for ways to reduce this negative trend. One method is non-intrusive load monitoring (NILM). This paper presents the use of artificial intelligence methods for the selection of information features and for the identification of operati...
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MDPI AG
2025-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/12/6807 |
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| author | Jacek Bartman Bogdan Kwiatkowski Damian Mazur Paweł Krutys Boguslaw Twarog |
| author_facet | Jacek Bartman Bogdan Kwiatkowski Damian Mazur Paweł Krutys Boguslaw Twarog |
| author_sort | Jacek Bartman |
| collection | DOAJ |
| description | The increasing energy intensity of the economy has led us to look for ways to reduce this negative trend. One method is non-intrusive load monitoring (NILM). This paper presents the use of artificial intelligence methods for the selection of information features and for the identification of operating electrical devices. A set of potential identification features was obtained from high-frequency measurements covering 12 types of electrical consumers and consisted of 218 features. From these, an identification vector was selected via the mRMR (minimum redundancy maximum relevance) method, which searches for features that are maximally correlated with the class and are as little correlated with each other as possible. Identification was realized by building a hybrid classifier using binary classifiers built from artificial neural networks and decision trees. The Accuracy, Precision, Recall, and F1 metrics were used to assess the quality of identification. The obtained values of the identification quality indicators confirm that it is possible to replace multiclass classification in NILM with binary classification without losing its quality. The use of binary classifiers allows for the identification of new devices without the need to change the classifier configuration. |
| format | Article |
| id | doaj-art-b85a32f31e684756bcc8767b6a6dd482 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-b85a32f31e684756bcc8767b6a6dd4822025-08-20T03:26:15ZengMDPI AGApplied Sciences2076-34172025-06-011512680710.3390/app15126807Use of Binary Classification in Non-Invasive Load MonitoringJacek Bartman0Bogdan Kwiatkowski1Damian Mazur2Paweł Krutys3Boguslaw Twarog4Institute of Computer Science, University of Rzeszow, 35-959 Rzeszow, PolandDepartment of Electrical Engineering and Fundamentals of Computer Science, Rzeszow University of Technology, 35-959 Rzeszow, PolandDepartment of Electrical Engineering and Fundamentals of Computer Science, Rzeszow University of Technology, 35-959 Rzeszow, PolandDepartment of Automation and Practical Electronics, State University of Technology and Economics in Jaroslaw, 37-500 Jaroslaw, PolandInstitute of Computer Science, University of Rzeszow, 35-959 Rzeszow, PolandThe increasing energy intensity of the economy has led us to look for ways to reduce this negative trend. One method is non-intrusive load monitoring (NILM). This paper presents the use of artificial intelligence methods for the selection of information features and for the identification of operating electrical devices. A set of potential identification features was obtained from high-frequency measurements covering 12 types of electrical consumers and consisted of 218 features. From these, an identification vector was selected via the mRMR (minimum redundancy maximum relevance) method, which searches for features that are maximally correlated with the class and are as little correlated with each other as possible. Identification was realized by building a hybrid classifier using binary classifiers built from artificial neural networks and decision trees. The Accuracy, Precision, Recall, and F1 metrics were used to assess the quality of identification. The obtained values of the identification quality indicators confirm that it is possible to replace multiclass classification in NILM with binary classification without losing its quality. The use of binary classifiers allows for the identification of new devices without the need to change the classifier configuration.https://www.mdpi.com/2076-3417/15/12/6807feature selectionartificial intelligenceneural networksdecision trees |
| spellingShingle | Jacek Bartman Bogdan Kwiatkowski Damian Mazur Paweł Krutys Boguslaw Twarog Use of Binary Classification in Non-Invasive Load Monitoring Applied Sciences feature selection artificial intelligence neural networks decision trees |
| title | Use of Binary Classification in Non-Invasive Load Monitoring |
| title_full | Use of Binary Classification in Non-Invasive Load Monitoring |
| title_fullStr | Use of Binary Classification in Non-Invasive Load Monitoring |
| title_full_unstemmed | Use of Binary Classification in Non-Invasive Load Monitoring |
| title_short | Use of Binary Classification in Non-Invasive Load Monitoring |
| title_sort | use of binary classification in non invasive load monitoring |
| topic | feature selection artificial intelligence neural networks decision trees |
| url | https://www.mdpi.com/2076-3417/15/12/6807 |
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