Multi-Channel Recurrent Convolutional Neural Networks for Energy Disaggregation
Power consumption signals of household appliances are characterized by randomly occurring events (e.g. switch-on events), making timeseries modeling a demanding process. In this paper, we propose a convolutional neural network (CNN)-based architecture with inputs and outputs formed as data sequences...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8740964/ |
| _version_ | 1818736678841352192 |
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| author | Maria Kaselimi Eftychios Protopapadakis Athanasios Voulodimos Nikolaos Doulamis Anastasios Doulamis |
| author_facet | Maria Kaselimi Eftychios Protopapadakis Athanasios Voulodimos Nikolaos Doulamis Anastasios Doulamis |
| author_sort | Maria Kaselimi |
| collection | DOAJ |
| description | Power consumption signals of household appliances are characterized by randomly occurring events (e.g. switch-on events), making timeseries modeling a demanding process. In this paper, we propose a convolutional neural network (CNN)-based architecture with inputs and outputs formed as data sequences taking into consideration an appliance's previous states for better estimation of its current state. Furthermore, the proposed model endows CNN models with a recurrent property in order to better capture energy signal interdependencies. Using a multi-channel CNN architecture fed with additional variables related to power consumption (current, reactive, and apparent power), additionally to active power, overall performance, robustness to noise and convergence times are improved. The experimental results prove the proposed method's superiority compared to the current state of the art. |
| first_indexed | 2024-12-18T00:40:58Z |
| format | Article |
| id | doaj.art-8aeecf94ca3b4d0399b2e0a13a149331 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-18T00:40:58Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8aeecf94ca3b4d0399b2e0a13a1493312022-12-21T21:26:53ZengIEEEIEEE Access2169-35362019-01-017810478105610.1109/ACCESS.2019.29237428740964Multi-Channel Recurrent Convolutional Neural Networks for Energy DisaggregationMaria Kaselimi0Eftychios Protopapadakis1Athanasios Voulodimos2https://orcid.org/0000-0002-0632-9769Nikolaos Doulamis3Anastasios Doulamis4School of Rural and Surveying Engineering, National Technical University of Athens, Athens, GreeceSchool of Rural and Surveying Engineering, National Technical University of Athens, Athens, GreeceDepartment of Informatics and Computer Engineering, University of West Attica, Athens, GreeceSchool of Rural and Surveying Engineering, National Technical University of Athens, Athens, GreeceSchool of Rural and Surveying Engineering, National Technical University of Athens, Athens, GreecePower consumption signals of household appliances are characterized by randomly occurring events (e.g. switch-on events), making timeseries modeling a demanding process. In this paper, we propose a convolutional neural network (CNN)-based architecture with inputs and outputs formed as data sequences taking into consideration an appliance's previous states for better estimation of its current state. Furthermore, the proposed model endows CNN models with a recurrent property in order to better capture energy signal interdependencies. Using a multi-channel CNN architecture fed with additional variables related to power consumption (current, reactive, and apparent power), additionally to active power, overall performance, robustness to noise and convergence times are improved. The experimental results prove the proposed method's superiority compared to the current state of the art.https://ieeexplore.ieee.org/document/8740964/Convolutional neural network (CNN)deep learningenergy disaggregationload monitoringNILMpower |
| spellingShingle | Maria Kaselimi Eftychios Protopapadakis Athanasios Voulodimos Nikolaos Doulamis Anastasios Doulamis Multi-Channel Recurrent Convolutional Neural Networks for Energy Disaggregation IEEE Access Convolutional neural network (CNN) deep learning energy disaggregation load monitoring NILM power |
| title | Multi-Channel Recurrent Convolutional Neural Networks for Energy Disaggregation |
| title_full | Multi-Channel Recurrent Convolutional Neural Networks for Energy Disaggregation |
| title_fullStr | Multi-Channel Recurrent Convolutional Neural Networks for Energy Disaggregation |
| title_full_unstemmed | Multi-Channel Recurrent Convolutional Neural Networks for Energy Disaggregation |
| title_short | Multi-Channel Recurrent Convolutional Neural Networks for Energy Disaggregation |
| title_sort | multi channel recurrent convolutional neural networks for energy disaggregation |
| topic | Convolutional neural network (CNN) deep learning energy disaggregation load monitoring NILM power |
| url | https://ieeexplore.ieee.org/document/8740964/ |
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