Artificial Intelligence (AI)-based identification of appliances in households through NILM

Mahmud, Asm Ashraf

doi:10.1109/gpecom55404.2022.9815626

Cited by 2 publications

(1 citation statement)

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“…Here, three parameters, precision (PR), recall (RCL), and F β score (FS) are defined to evaluate the load identification performance [33]. The calculations are as follows:…”

Section: Evaluation Metricsmentioning

confidence: 99%

A Nonintrusive Load Identification Method Based on Improved Gramian Angular Field and ResNet18

Wang

Shu

2023

Electronics

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Image classification methods based on deep learning have been widely used in the study of nonintrusive load identification. However, in the process of encoding the load electrical signals into images, how to fully retain features of the raw data and thus increase the recognizability of loads carried with very similar current signals are still challenging, and the loss of load features will cause the overall accuracy of load identification to decrease. To deal with this problem, this paper proposes a nonintrusive load identification method based on the improved Gramian angular field (iGAF) and ResNet18. In the proposed method, fast Fourier transform is used to calculate the amplitude spectrum and the phase spectrum to reconstruct the pixel matrices of the B channel, G channel, and R channel of generated GAF images so that the color image fused by the three channels contains more information. This improvement to the GAF method enables generated images to retain the amplitude feature and phase feature of the raw data that are usually missed in the general GAF image. ResNet18 is trained with iGAF images for nonintrusive load identification. Experiments are conducted on two private datasets, ESEAD and EMCAD, and two public datasets, PLAID and WHITED. Experimental results suggest that the proposed method performs well on both private and public datasets, achieving overall identification accuracies of 99.545%, 99.375%, 98.964%, and 100% on the four datasets, respectively. In particular, the method demonstrates significant identification effects for loads with similar current waveforms in private datasets.

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“…Here, three parameters, precision (PR), recall (RCL), and F β score (FS) are defined to evaluate the load identification performance [33]. The calculations are as follows:…”

Section: Evaluation Metricsmentioning

confidence: 99%