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dc.contributor.authorKianpoor, Nasrin
dc.contributor.authorHoff, Bjarte
dc.contributor.authorØstrem, Trond
dc.date.accessioned2023-08-17T08:26:55Z
dc.date.available2023-08-17T08:26:55Z
dc.date.issued2023-02-10
dc.description.abstractIdentifying flexible loads, such as a heat pump, has an essential role in a home energy management system. In this study, an adaptive ensemble filtering framework integrated with long short-term memory (LSTM) is proposed for identifying flexible loads. The proposed framework, called AEFLSTM, takes advantage of filtering techniques and the representational power of LSTM for load disaggregation by filtering noise from the total power and learning the long-term dependencies of flexible loads. Furthermore, the proposed framework is adaptive and searches ensemble filtering techniques, including discrete wavelet transform, low-pass filter, and seasonality decomposition, to find the best filtering method for disaggregating different flexible loads (e.g., heat pumps). Experimental results are presented for estimating the electricity consumption of a heat pump, a refrigerator, and a dishwasher from the total power of a residential house in British Columbia (a publicly available use case). The results show that AEFLSTM can reduce the loss error (mean absolute error) by 57.4%, 44%, and 55.5% for estimating the power consumption of the heat pump, refrigerator, and dishwasher, respectively, compared to the stand-alone LSTM model. The proposed approach is used for another dataset containing measurements of an electric vehicle to further support the validity of the method. AEFLSTM is able to improve the result for disaggregating an electric vehicle by 22.5%.en_US
dc.identifier.citationKianpoor, Hoff, Østrem. Deep Adaptive Ensemble Filter for Non-Intrusive Residential Load Monitoring. Sensors. 2023;23(4)en_US
dc.identifier.cristinIDFRIDAID 2155165
dc.identifier.doi10.3390/s23041992
dc.identifier.issn1424-8220
dc.identifier.urihttps://hdl.handle.net/10037/30015
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.relation.journalSensors
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleDeep Adaptive Ensemble Filter for Non-Intrusive Residential Load Monitoringen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)