Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1090
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dc.contributor.authorSenarathna, M-
dc.contributor.authorHerath, M-
dc.contributor.authorThilakanayake, H. D-
dc.contributor.authorLiyanage, M. H-
dc.contributor.authorAngammana, C. J-
dc.date.accessioned2022-02-10T06:13:30Z-
dc.date.available2022-02-10T06:13:30Z-
dc.date.issued2021-12-09-
dc.identifier.citationM. Senarathna, M. Herath, T. D. Thilakanayake, M. H. Liyanage and C. J. Angammana, "An Image Based Approach of Energy Signal Disaggregation Using Artificial Intelligence," 2021 IEEE 16th International Conference on Industrial and Information Systems (ICIIS), 2021, pp. 98-103, doi: 10.1109/ICIIS53135.2021.9660638.en_US
dc.identifier.issn2164-7011-
dc.identifier.urihttp://rda.sliit.lk/handle/123456789/1090-
dc.description.abstractNon-Intrusive Load Monitoring (NILM) is the real-time monitoring of energy consumption data of individual appliances through the decomposition of composite energy signal captured at the household smart energy meter. Most of the existing NILM techniques utilize one-dimensional (1D) time-series signal analysis to predict the individual appliance energy signals. The utilization of image-based methods for the disaggregation of energy signals is a relatively new approach in the NILM domain. This paper presents a study of a novel computer vision-based Artificial Intelligence (AI) approach when compared to the traditional time series-based NILM methods. Gramian Angular Fields (GAF) and Recurrence Plots (RP) have been widely used in recent literature to encode time series signals as images. Novel image classification techniques with the use of Convolutional Neural Networks (CNN) simplify the extraction of nuclear load features from encoded two-dimensional (2D) images. The results considered the indices validation accuracy and validation loss in comparing the performance of different vision-based AI approaches. The results reveal that Gramian Angular Difference Field (GADF) outperforms both Gramian Angular Summation Field (GASF) and RP with a training accuracy of 97.9% and a validation accuracy of 94.2%. A comprehensive analysis and comparison are presented with an in-depth evaluation using multi-state appliances and it was concluded that GADF is the most suitable 1D to 2D conversion method for the representation of time series energy data for disaggregation purposes.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2021 IEEE 16th International Conference on Industrial and Information Systems (ICIIS);Pages 98-103-
dc.subjectImage Based Approachen_US
dc.subjectEnergy Signal Disaggregationen_US
dc.subjectUsing Artificial Intelligenceen_US
dc.titleAn Image Based Approach of Energy Signal Disaggregation Using Artificial Intelligenceen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ICIIS53135.2021.9660638en_US
Appears in Collections:Department of Mechanical Engineering-Scopes
Research Papers
Research Papers - Department of Mechanical Engineering
Research Papers - SLIIT Staff Publications

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