A Non-Intrusive Load Monitoring Method Based on Feature Fusion and SE-ResNet
Tie Chen, Huayuan Qin, Xianshan Li, Wenhao Wan, Wenwei Yan
Abstract
In the study of non-intrusive load monitoring, using a single feature for identification can lead to insignificant differentiation of similar loads; however, multi-feature fusion can pool the advantages of different features to improve identification accuracy. Based on this, this paper proposes a recognition method based on feature fusion and matrix heat maps, using V-I traces, phase and amplitude of odd harmonics, and fundamental amplitude. These are converted into matrix heat maps, which can retain both large and small eigenvalues of the same feature for different loads and can retain different features. The matrix heat map is recognized by using SE-ResNet18, which avoids the problem of the classical CNN depth being too deep, causing network degradation and being difficult to train, and achieves trauma-free monitoring of home loads. Finally, the model is validated using the PLAID and REDD datasets, and the average recognition accuracy is 96.24% and 96.4%, respectively, with significant recognition effects for loads with similar V-I trajectories and multi-state loads.