Selection of Features Based on Electric Power Quantities for Non-Intrusive Load Monitoring
Non-intrusive load monitoring (NILM) is a process of determining the operating states and the energy consumption of single electric devices using a single energy meter providing aggregate load measurements. Due to the large spread of power electronic-based and nonlinear devices connected to the netw...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/2/533 |
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| author | Barbara Cannas Sara Carcangiu Daniele Carta Alessandra Fanni Carlo Muscas |
| author_facet | Barbara Cannas Sara Carcangiu Daniele Carta Alessandra Fanni Carlo Muscas |
| author_sort | Barbara Cannas |
| collection | DOAJ |
| description | Non-intrusive load monitoring (NILM) is a process of determining the operating states and the energy consumption of single electric devices using a single energy meter providing aggregate load measurements. Due to the large spread of power electronic-based and nonlinear devices connected to the network, the time signals of both voltage and current are typically non-sinusoidal. The effectiveness of a NILM algorithm strongly depends on determining a set of discriminative features. In this paper, voltage and current signals were combined to define, according to the definitions provided in Standard IEEE 1459, different power quantities, that can be used to distinguish different types of appliance. Multi-layer perceptron (MLP) classifiers were trained to solve the appliance detection problem as a multi-class event classification problem, varying the electric features in input. This allowed to select an optimal set of features guarantying good classification performance in identifying typical electric loads. |
| first_indexed | 2024-03-09T05:42:55Z |
| format | Article |
| id | doaj.art-2c96cef597ef4084a0d64236b284c9d0 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T05:42:55Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-2c96cef597ef4084a0d64236b284c9d02023-12-03T12:23:40ZengMDPI AGApplied Sciences2076-34172021-01-0111253310.3390/app11020533Selection of Features Based on Electric Power Quantities for Non-Intrusive Load MonitoringBarbara Cannas0Sara Carcangiu1Daniele Carta2Alessandra Fanni3Carlo Muscas4Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, ItalyDepartment of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, ItalyDepartment of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, ItalyDepartment of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, ItalyDepartment of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, ItalyNon-intrusive load monitoring (NILM) is a process of determining the operating states and the energy consumption of single electric devices using a single energy meter providing aggregate load measurements. Due to the large spread of power electronic-based and nonlinear devices connected to the network, the time signals of both voltage and current are typically non-sinusoidal. The effectiveness of a NILM algorithm strongly depends on determining a set of discriminative features. In this paper, voltage and current signals were combined to define, according to the definitions provided in Standard IEEE 1459, different power quantities, that can be used to distinguish different types of appliance. Multi-layer perceptron (MLP) classifiers were trained to solve the appliance detection problem as a multi-class event classification problem, varying the electric features in input. This allowed to select an optimal set of features guarantying good classification performance in identifying typical electric loads.https://www.mdpi.com/2076-3417/11/2/533non-intrusive load monitoringnonlinear devicesfeature selectionmachine learningpower definitions |
| spellingShingle | Barbara Cannas Sara Carcangiu Daniele Carta Alessandra Fanni Carlo Muscas Selection of Features Based on Electric Power Quantities for Non-Intrusive Load Monitoring Applied Sciences non-intrusive load monitoring nonlinear devices feature selection machine learning power definitions |
| title | Selection of Features Based on Electric Power Quantities for Non-Intrusive Load Monitoring |
| title_full | Selection of Features Based on Electric Power Quantities for Non-Intrusive Load Monitoring |
| title_fullStr | Selection of Features Based on Electric Power Quantities for Non-Intrusive Load Monitoring |
| title_full_unstemmed | Selection of Features Based on Electric Power Quantities for Non-Intrusive Load Monitoring |
| title_short | Selection of Features Based on Electric Power Quantities for Non-Intrusive Load Monitoring |
| title_sort | selection of features based on electric power quantities for non intrusive load monitoring |
| topic | non-intrusive load monitoring nonlinear devices feature selection machine learning power definitions |
| url | https://www.mdpi.com/2076-3417/11/2/533 |
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