Research on Non-Intrusive Combined Load Decomposition and Identification Method Based on Deep Learning

To enable fine-grained electricity management on the user side under the dual-carbon strategy and address the inherent limitations of traditional non-intrusive load monitoring (NILM) methods in multi-load parallel operation scenarios, this paper proposes a novel synergistically optimized framework. The framework sequentially integrates three core modules to tackle the key challenges of load identification: SSA-VMD-based load quantity estimation, CNN-LSTM-Attention-based current separation, and GAF-ResNet18-based load recognition. First, the Sparrow Search Algorithm optimizes Variational Mode Decomposition parameters, combined with Pearson-PCA, to accurately estimate the number of operating loads in mixed-power signals without prior knowledge. Second, a hybrid CNN-LSTM-Attention model extracts deep spatial-temporal features from the aggregated current spectrogram, enabling high-fidelity separation and reconstruction of individual load current waveforms. Third, the separated current signals are transformed into Gramian Angular Field images and classified by a ResNet18 network for robust load identification. The framework’s efficacy is rigorously validated on both the public PLAID dataset and a self-constructed laboratory dataset, covering diverse dual-load and triple-load operating conditions. Results demonstrate that the method achieves R2 coefficients exceeding 0.9 for current waveform reconstruction and maintains load recognition accuracy above 91% across all test cases, significantly improving identification performance under complex electricity consumption conditions. This high-performance load disaggregation provides critical data support for advanced grid applications, including demand response, load forecasting, and distribution network planning, thereby contributing to the intelligence and efficiency of future power systems.