Low frequency-based energy disaggregation using sliding windows and deep learning
The issue of controlling energy use is becoming extremely important. People’s behavior is one of the most important elements influencing electric energy usage in the residential sector, one of the most significant energy consumers globally. The building’s energy usage could be reduced by using feedb...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2022-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/18/e3sconf_icies2022_01020.pdf |
| _version_ | 1811232514707030016 |
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| author | Laouali Inoussa Habou Bot Karol Ruano Antonio Ruano Maria da Graça Bennani Saad Dosse El Fadili Hakim |
| author_facet | Laouali Inoussa Habou Bot Karol Ruano Antonio Ruano Maria da Graça Bennani Saad Dosse El Fadili Hakim |
| author_sort | Laouali Inoussa Habou |
| collection | DOAJ |
| description | The issue of controlling energy use is becoming extremely important. People’s behavior is one of the most important elements influencing electric energy usage in the residential sector, one of the most significant energy consumers globally. The building’s energy usage could be reduced by using feedback programs. Non-Intrusive Load Monitoring (NILM) approaches have emerged as one of the most viable options for energy disaggregation. This paper presents a deep learning algorithm using Long Short-Term Memory (LSTM) models for energy disaggregation. It employs low-frequency sampling power data collected in a private house. The aggregated active and reactive powers are used as inputs in a sliding window. The obtained results show that the proposed approach gives high performances in term of recognizing the devices' operating states and predicting the energy consumed by each device. |
| first_indexed | 2024-04-12T11:04:11Z |
| format | Article |
| id | doaj.art-1e75166825714aaa8d981ca9b860f922 |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-04-12T11:04:11Z |
| publishDate | 2022-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-1e75166825714aaa8d981ca9b860f9222022-12-22T03:35:50ZengEDP SciencesE3S Web of Conferences2267-12422022-01-013510102010.1051/e3sconf/202235101020e3sconf_icies2022_01020Low frequency-based energy disaggregation using sliding windows and deep learningLaouali Inoussa Habou0Bot Karol1Ruano Antonio2Ruano Maria da Graça3Bennani Saad Dosse4El Fadili Hakim5Faculty of Sciences & Technology, University of AlgarveFaculty of Sciences & Technology, University of AlgarveFaculty of Sciences & Technology, University of AlgarveFaculty of Sciences & Technology, University of AlgarveSIGER, Faculty of Sciences and Technology, Sidi Mohamed Ben AbdellahLIPI, ENS, Sidi Mohamed Ben AbdellahThe issue of controlling energy use is becoming extremely important. People’s behavior is one of the most important elements influencing electric energy usage in the residential sector, one of the most significant energy consumers globally. The building’s energy usage could be reduced by using feedback programs. Non-Intrusive Load Monitoring (NILM) approaches have emerged as one of the most viable options for energy disaggregation. This paper presents a deep learning algorithm using Long Short-Term Memory (LSTM) models for energy disaggregation. It employs low-frequency sampling power data collected in a private house. The aggregated active and reactive powers are used as inputs in a sliding window. The obtained results show that the proposed approach gives high performances in term of recognizing the devices' operating states and predicting the energy consumed by each device.https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/18/e3sconf_icies2022_01020.pdf |
| spellingShingle | Laouali Inoussa Habou Bot Karol Ruano Antonio Ruano Maria da Graça Bennani Saad Dosse El Fadili Hakim Low frequency-based energy disaggregation using sliding windows and deep learning E3S Web of Conferences |
| title | Low frequency-based energy disaggregation using sliding windows and deep learning |
| title_full | Low frequency-based energy disaggregation using sliding windows and deep learning |
| title_fullStr | Low frequency-based energy disaggregation using sliding windows and deep learning |
| title_full_unstemmed | Low frequency-based energy disaggregation using sliding windows and deep learning |
| title_short | Low frequency-based energy disaggregation using sliding windows and deep learning |
| title_sort | low frequency based energy disaggregation using sliding windows and deep learning |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2022/18/e3sconf_icies2022_01020.pdf |
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