Automated Non-Intrusive Load Monitoring System Using Stacked Neural Networks and Numerical Integration
Population growth and new consumer needs, among other factors, have lead to growing energy demand, without a concomitant increase in energy generation. This way, reduction and rationalization of energy consumption, especially by residential users, have become a global concern generating a need for d...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9265177/ |
| _version_ | 1818617261261324288 |
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| author | Suelene de Jesus do Carmo Adriana R. G. Castro |
| author_facet | Suelene de Jesus do Carmo Adriana R. G. Castro |
| author_sort | Suelene de Jesus do Carmo |
| collection | DOAJ |
| description | Population growth and new consumer needs, among other factors, have lead to growing energy demand, without a concomitant increase in energy generation. This way, reduction and rationalization of energy consumption, especially by residential users, have become a global concern generating a need for developing techniques for efficient management and distribution of the available energy. Non-Intrusive Load Monitoring (NILM) techniques have provided valuable information about energy consumption for power generation companies as well as consumers. Such information is important for making decisions related to sustainable use of energy resources. This study proposes an automated system based on Artificial Neural Network for performing some of the NILM tasks. A stacked neural network was developed to extract features of power signals of appliances to identify those in operation during a given period. This information is then used to disaggregate individual appliance loads through the total aggregate signal, and consumption is calculated through numerical integration. The system was tested using real data from two databases about appliances with On/Off, multi-level, and variable consumption patterns collected in low frequency. The performance metrics, resulting from identification and disaggregation tasks, demonstrate the efficiency of the proposed system. |
| first_indexed | 2024-12-16T17:02:53Z |
| format | Article |
| id | doaj.art-866f91f60caf48a7a442ee13ecaeec27 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T17:02:53Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-866f91f60caf48a7a442ee13ecaeec272022-12-21T22:23:41ZengIEEEIEEE Access2169-35362020-01-01821056621058110.1109/ACCESS.2020.30396399265177Automated Non-Intrusive Load Monitoring System Using Stacked Neural Networks and Numerical IntegrationSuelene de Jesus do Carmo0https://orcid.org/0000-0002-1584-9459Adriana R. G. Castro1https://orcid.org/0000-0001-5884-4511Institute of Technology, Federal University of Pará, Belém, BrazilInstitute of Technology, Federal University of Pará, Belém, BrazilPopulation growth and new consumer needs, among other factors, have lead to growing energy demand, without a concomitant increase in energy generation. This way, reduction and rationalization of energy consumption, especially by residential users, have become a global concern generating a need for developing techniques for efficient management and distribution of the available energy. Non-Intrusive Load Monitoring (NILM) techniques have provided valuable information about energy consumption for power generation companies as well as consumers. Such information is important for making decisions related to sustainable use of energy resources. This study proposes an automated system based on Artificial Neural Network for performing some of the NILM tasks. A stacked neural network was developed to extract features of power signals of appliances to identify those in operation during a given period. This information is then used to disaggregate individual appliance loads through the total aggregate signal, and consumption is calculated through numerical integration. The system was tested using real data from two databases about appliances with On/Off, multi-level, and variable consumption patterns collected in low frequency. The performance metrics, resulting from identification and disaggregation tasks, demonstrate the efficiency of the proposed system.https://ieeexplore.ieee.org/document/9265177/NILMAANNfeature extractionload identificationpattern recognition |
| spellingShingle | Suelene de Jesus do Carmo Adriana R. G. Castro Automated Non-Intrusive Load Monitoring System Using Stacked Neural Networks and Numerical Integration IEEE Access NILM AANN feature extraction load identification pattern recognition |
| title | Automated Non-Intrusive Load Monitoring System Using Stacked Neural Networks and Numerical Integration |
| title_full | Automated Non-Intrusive Load Monitoring System Using Stacked Neural Networks and Numerical Integration |
| title_fullStr | Automated Non-Intrusive Load Monitoring System Using Stacked Neural Networks and Numerical Integration |
| title_full_unstemmed | Automated Non-Intrusive Load Monitoring System Using Stacked Neural Networks and Numerical Integration |
| title_short | Automated Non-Intrusive Load Monitoring System Using Stacked Neural Networks and Numerical Integration |
| title_sort | automated non intrusive load monitoring system using stacked neural networks and numerical integration |
| topic | NILM AANN feature extraction load identification pattern recognition |
| url | https://ieeexplore.ieee.org/document/9265177/ |
| work_keys_str_mv | AT suelenedejesusdocarmo automatednonintrusiveloadmonitoringsystemusingstackedneuralnetworksandnumericalintegration AT adrianargcastro automatednonintrusiveloadmonitoringsystemusingstackedneuralnetworksandnumericalintegration |