A Non-Intrusive Load Identification Method Based on the Fusion of Steady-State Features and Lightweight Network

Non-intrusive load monitoring (NILM) is essential for smart grid demand-side management and energy conservation, yet existing methods suffer from limited feature discrimination, ambiguous identification of similar electrical appliances, and difficulty balancing model accuracy and lightweight deployment. To address these issues, this paper proposes a dual-branch lightweight load identification method fusing steady-state features and lightweight network. Firstly, V-I trajectory images are generated via standardized transformation and two-dimensional histogram logarithmic mapping, while steady-state characteristics, including active power, reactive power, trajectory area and intermediate section slope, are extracted. Then, a dual-branch network is constructed, where the visual branch adopts depthwise separable convolution and lightweight multi-head attention to mine global trajectory features, and the numerical branch uses fully connected layers to encode steady-state features; feature concatenation fusion is adopted to complete appliance classification. The experimental results on the Plug Load Appliance Identification Dataset (PLAID dataset) show that the proposed method achieves a recognition accuracy of 95.35% with only 0.17M parameters, outperforming standard and medium convolutional neural network (CNN) models. Ablation experiments verify that steady-state feature fusion effectively improves the identification accuracy of easily confused and small-sample loads. The proposed method realizes high-precision and lightweight load identification, which is suitable for edge deployment in smart meters and has practical application value for intelligent power management.