Authors:
Chenchen Zhang
1
;
Yujun Song
2
;
Dong Wang
3
;
Shifang Song
2
;
Xuesong Pan
3
and
Lanzhou Liu
1
Affiliations:
1
Ocean University of China, Qingdao, China
;
2
Qingdao Haier Air Conditioner Co., Ltd, Qingdao, China
;
3
Qingdao Haier Air Conditioner Co., Ltd, State Key Laboratory of Digital Household Appliances, Qingdao, China
Keyword(s):
NILM, V-If trajectories, load identification, attention mechanism.
Abstract:
In recent years, deep learning has been widely applied in various fields, including the field of load recognition. Machine learning methods such as SVM and K-means, as well as various neural network approaches, have shown promising results. However, due to the significant differences among similar appliances and the existence of multiple operating states for each appliance, misjudgments often occur during load recognition. Therefore, this paper proposes a preprocessing method that transforms current-voltage data into V-If trajectories. Additionally, a non-intrusive load recognition algorithm is presented, which incorporates a self-designed convolutional neural network (CNN), a hybrid attention mechanism (ECA_NET and Spatial attention mechanism, ECA-SAM), and a hybrid loss function (Center Loss and ArcFace, CA). The effectiveness of this approach is demonstrated through simulation experiments conducted on the PLAID dataset, achieving a remarkable 98% accuracy in the identification of
electrical appliances.
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