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Deep learning based non-intrusive load monitoring for a three-phase system

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dc.contributor.author Gowrienanthan, B
dc.contributor.author Kiruthihan, N
dc.contributor.author Rathnayake, KDIS
dc.contributor.author Kiruthikan, S
dc.contributor.author Logeeshan, V
dc.contributor.author Kumarawadu, S
dc.contributor.author Wanigasekara, C
dc.date.accessioned 2023-12-01T06:00:54Z
dc.date.available 2023-12-01T06:00:54Z
dc.date.issued 2023
dc.identifier.citation Gowrienanthan, B., Kiruthihan, N., Rathnayake, K. D. I. S., Kiruthikan, S., Logeeshan, V., Kumarawadu, S., & Wanigasekara, C. (2023). Deep Learning Based Non-Intrusive Load Monitoring for a Three-Phase System. IEEE Access, 11, 49337–49349. https://doi.org/10.1109/ACCESS.2023.3276475 en_US
dc.identifier.issn 2169-3536 en_US
dc.identifier.uri http://dl.lib.uom.lk/handle/123/21867
dc.description.abstract Non-Intrusive Load Monitoring (NILM) is a method to determine the power consumption of individual appliances from the overall power consumption measured by a single measurement device, which is usually the main meter. Increase in the adoption of smart meters has facilitated large scale implementation of NILM, which can provide information about individual loads to the utilities and consumers. This will lead to significant energy savings as well as better demand-side management. Researchers have proposed several methods and have successfully implemented NILM for residential sectors that have a single-phase supply. However, NILM has not been successfully implemented for industrial and commercial buildings that have a three-phase supply, due to several challenges. These buildings consume significant amount of power and implementing NILM to these buildings has the potential to yield substantial benefits. In this paper, we propose a novel deep learning-based approach to address some of the key challenges in implementing NILM for buildings that have a three-phase supply. Our approach introduces an ensemble learning technique that does not require training of multiple neural network models, which reduces the computational requirements and makes it economically feasible. The model was tested on a three-phase system that consists of both three- phase loads and single-phase loads. The results show significant improvement in load disaggregation compared to the existing methods and indicate its applicability. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.subject NILM en_US
dc.subject neural networks en_US
dc.subject deep learning en_US
dc.subject ensemble learning en_US
dc.subject load disaggregation en_US
dc.title Deep learning based non-intrusive load monitoring for a three-phase system en_US
dc.type Article-Full-text en_US
dc.identifier.year 2023 en_US
dc.identifier.journal IEEE Access en_US
dc.identifier.volume 11 en_US
dc.identifier.database IEE Xplore en_US
dc.identifier.pgnos 49337-49349 en_US
dc.identifier.doi 10.1109/ACCESS.2023.3276475 en_US


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