Abstract
Non-Intrusive Load Monitoring aims to extract the energy consumption of individual electrical appliances through disaggregation of the total power consumption as measured by a single smart meter at a household. Deep neural networks and especially Convolutional Neural Networks (CNNs) have become popular in solving the Non-Intrusive Load Monitoring problem. However, since NILM is a time series problem mostly 1-D CNNs have been utilized, thus not fully exploiting the capability of CNNs which are advantageous mostly in 2-D data such as images. Therefore, in the proposed architecture 2-D signatures of low frequency active and reactive power are utilized. The proposed architecture was evaluated on the AMPds2 dataset reporting performances up-to 96.1% in terms of estimation accuracy outperforming all previously reported approaches on the same dataset by 1.1%, in terms of absolute improvement.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
