尽管非侵入式负荷监测（non-intrusive load monitoring，NILM）已经得到了广泛的研究，然而现有的非侵入式负荷监测模型存在多工作状态电器预测困难的问题，导致预测精度显著降低。为此设计了一种基于多算法融合的非侵入式负荷监测模型。首先，对REDD低频数据集进行基于时间的插值、过采样等方式预处理数据。其次，模型采用图卷积神经网络（graph convolutional networks，GCN）和卷积神经网络（convolutional neural network，CNN）提取功率特征，将功率特征输入到自注意力机制和长短期记忆网络（long short-term memory，LSTM）中，有效提取了输入信号中的关键特征，提高多工作状态电器的预测精度。最后，利用预处理后REDD低频数据集进行仿真验证，实验结果表明所提出的模型在MAE、SAE和R^{\mathrm{2}}指标上均优于对比模型，能够有效实现负荷分解。

Despite extensive research on non-intrusive load monitoring(NILM), existing models face challenges in accurately predicting multiple operational states of appliances, leading to significant decreases in prediction accuracy. To address this issue, this paper proposes non-intrusive load monitoring model based on multi-algorithm fusion. Firstly, the REDD low-frequency dataset is preprocessed using time-based interpolation and oversampling. Secondly, the model employs graph convolutional networks(GCN) and convolutional neural networks(CNN) to extract power features, which are then fed into a self-attention mechanism and long short-term memory(LSTM) networks. This effectively captures the key features of the input signals, thereby improving the prediction accuracy for appliances with multiple operational states. Finally, simulation verification is conducted using the preprocessed REDD low-frequency dataset. The experimental results indicate that the proposed model outperforms comparative models in terms of MAE, SAE, and R^{\mathrm{2}}, demonstrating its effectiveness in load disaggregation.