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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.<\/jats:p>","DOI":"10.1051\/e3sconf\/202235101020","type":"journal-article","created":{"date-parts":[[2022,5,24]],"date-time":"2022-05-24T07:57:07Z","timestamp":1653379027000},"page":"01020","source":"Crossref","is-referenced-by-count":1,"title":["Low frequency-based energy disaggregation using sliding windows and deep learning"],"prefix":"10.1051","volume":"351","author":[{"given":"Inoussa Habou","family":"Laouali","sequence":"first","affiliation":[]},{"given":"Karol","family":"Bot","sequence":"additional","affiliation":[]},{"given":"Antonio","family":"Ruano","sequence":"additional","affiliation":[]},{"given":"Maria da Gra\u00e7a","family":"Ruano","sequence":"additional","affiliation":[]},{"given":"Saad Dosse","family":"Bennani","sequence":"additional","affiliation":[]},{"given":"Hakim","family":"El Fadili","sequence":"additional","affiliation":[]}],"member":"250","published-online":{"date-parts":[[2022,5,24]]},"reference":[{"key":"R1","doi-asserted-by":"crossref","unstructured":"Santiago I., Lopez-Rodriguez M. 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