近年来，以深度学习为基础的负荷分解方法得到了广泛应用。但是，目前的研究主要局限于传统欧氏空间序列的输入，难以精确刻画电气设备工作过程中的时序相关性，从而降低了对电气设备的解析精度。此外，家电开关动作可能在时间序列数据中产生长距离影响，但现有模型很少考虑负荷数据的长距离依赖问题。针对上述问题提出了一种基于图卷积网络（graph convolutional network，GCN）和双向长短期记忆网络（bidirectional long short-term memory，BiLSTM）的非侵入式负荷分解模型。该方法基于图理论将总负荷序列转换为包含节点和边的图结构数据，充分考虑节点之间的相关性特征，并利用GCN进行特征提取。同时，引入BiLSTM神经网络以处理长时间序列数据的局限性。通过算例分析验证了所提模型在分解精度和效果上显著优于传统方法。

In recent years, load disaggregation methods based on deep learning have been widely applied. However, current researches mainly focuse on inputs from traditional Euclidean space sequences, which struggle to accurately capture temporal correlations during the operation of electrical devices, thereby reducing the resolution accuracy of electrical equipment analysis. Furthermore, the switching actions of household appliances may have long-distance impacts in time series data, yet existing models often overlook the long-distance dependency issues in load data. To address these challenges, a non-intrusive load disaggregation model is proposed based on graph convolutional network (GCN) and bidirectional long short-term memory (BiLSTM). This method transforms the total load sequence into graph-structured data containing nodes and edges using graph theory, effectively considering inter-node correlation features and utilizing GCN for feature extraction. Additionally, BiLSTM neural networks are introduced to handle the limitations of long-term time series data. Case analysis demonstrates that the proposed model significantly outperforms traditional methods in terms of disaggregation accuracy and effectiveness.