Research on non-invasive load monitoring based on convolutional neural network

Zhou, Yajun; Sun, Mingyang; Li, Pengyun; Cui, Wenpeng; Li, Rui; Zheng, Zhe; Jing, Zhen; Zhu, Hongxia

doi:10.1109/cisce55963.2022.9851091

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…For instance, De Baets et al [16] introduced the weighted pixelization of voltage-current trajectories, utilizing an n × n grid to cover continuous trajectories and employing a convolutional neural network for appliance identification. Zhou et al [17] focused on the harmonic features of current, creating a combination matrix of voltage-current trajectories and harmonic features for accurate appliance identification using a multilayer convolutional neural network. Liu et al [18] transformed V-I trajectories into visual representations in the HSV color space and fine-tuned a pretrained convolutional neural network for successful classification of color images of V-I trajectories.…”

Section: Introductionmentioning

confidence: 99%

Research on non-intrusive load identification method based on multi-feature fusion with improved shufflenetv2

Wang,

Shu

2024

Meas. Sci. Technol.

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Non-Intrusive Load Monitoring (NILM) is a significant advancement in energy conservation and smart electricity usage as it enables the identification of load status and equipment type without the need for extensive sensing instruments. As one of the important steps of non-intrusive load monitoring, non-intrusive load identification plays a crucial role in the correct identification of electrical appliances. In this paper, a novel approach is presented to amalgamate multiple appliance features into a new identification feature, which is then used for appliance recognition through an improved lightweight deep learning model. This addresses the issue of low accuracy when using a single feature for appliance identification. The core concept involves initially capturing the current data of an appliance during steady state operation and fusing the image features of the appliance encoded by Gramian Summation Angular Field (GASF), Gramian Difference Angular Field (GADF), and Markov transition field (MTF) into new recognition features for each appliance using average weighting. Subsequently, a shufflenetv2 lightweight deep learning model based on the Squeeze -and-Excitation (SE) module is used to mine the constructed load feature information for the load classification task. The method is then experimentally validated on the Self-test Dataset, PLAID, and WHITED dataset, resulting in recognition accuracies of 100%, 98.214%, and 99.745%, respectively. These results demonstrate the effectiveness of the proposed method in significantly improving recognition performance compared to the original approach.

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Section: Introductionmentioning

confidence: 99%

Research on non-intrusive load identification method based on multi-feature fusion with improved shufflenetv2

Wang,

Shu

2024

Meas. Sci. Technol.

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“…The experiments were conducted on common household appliances to verify the efficacy of the proposed method. Zhou et al [16] proposed a method for electrical recognition by combining V-I trajectory features and current harmonic features through a binarization process to form a combination matrix, which was then input into a convolutional neural network (CNN). This approach improves the accuracy of electrical recognition by integrating both time-domain and frequency-domain features.…”

Section: Introductionmentioning

confidence: 99%

Non-Intrusive Load Identification Method Based on KPCA-IGWO-RF

Yuan

et al. 2023

Energies

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Non-invasive load monitoring (NILM) represents a crucial technology in enabling smart electricity consumption. In response to the challenges posed by high feature redundancy, low identification accuracy, and the high computational costs associated with current load identification models, a novel load identification model based on kernel principal component analysis (KPCA) and random forest (RF) optimized by improved Grey Wolf Optimizer (IGWO) is proposed. Initially, 17 steady-state load characteristics were selected as discrimination indexes. KPCA was subsequently employed to reduce the dimension of the original data and diminish the correlation between the feature indicators. Then, the dimension reduction in load data was classified by RF. In order to improve the performance of the classifier, IGWO was used to optimize the parameters of the RF classifier. Finally, the proposed model was implemented to identify 25 load states consisting of seven devices. The experimental results demonstrate that the identification accuracy of this method is up to 96.8% and the Kappa coefficient is 0.9667.

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Non-Intrusive Load Monitoring Based on Diffusion Model for Transfer Learning among Buildings

Cheng,

Xu,

et al. 2023

2023 2nd Asian Conference on Frontiers of Power and Energy (ACFPE)

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Research on non-invasive load monitoring based on convolutional neural network

Cited by 3 publications

References 9 publications

Research on non-intrusive load identification method based on multi-feature fusion with improved shufflenetv2

Research on non-intrusive load identification method based on multi-feature fusion with improved shufflenetv2

Non-Intrusive Load Identification Method Based on KPCA-IGWO-RF

Non-Intrusive Load Monitoring Based on Diffusion Model for Transfer Learning among Buildings

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