【摘要】 随着智能电网与可再生能源的迅速发展，非侵入式负荷分解技术在电力资源优化配置中展现出重要应用潜力。然而，现有方法在充分建模长时序依赖，精确捕捉多样化电器特征，以及确保功率与状态预测一致性方面仍面临挑战。基于此，提出了一种基于Transformer与多尺度注意力卷积网络的多任务非侵入式负荷分解架构，该架构包含功率分解与状态识别两个并行分支。功率分解分支基于Transformer结构，利用其多头自注意力机制深入捕捉负荷序列的长距离依赖与动态模式。状态识别分支则针对性设计了一种卷积神经网络结构，通过融合多尺度卷积模块与通道-空间注意力机制，有效提取电器开关事件的关键特征。为增强任务间的一致性，模型将两个独立分支的功率预测值与状态概率进行逐点相乘，利用状态信息直接约束功率输出，显著抑制了电器关闭状态下的功率误报。在参考能源分解数据集（reference energy disaggregation dataset, REDD）和英国家庭电器级用电数据集（UK domestic appliance-level electricity, UK-DALE）上的实验结果表明，所提模型在平均绝对误差（mean absolute error, MAE）、信号聚合误差（signal aggregate error, SAE）以及F₁分数等评估指标上均优于现有主流方法。更多还原

【Abstract】 Non-intrusive load disaggregation technology offers significant potential for power resource optimization in smart grids. However, several challenges are faced by existing methods. Long-term temporal dependencies are inadequately modeled. Diverse appliance features are captured imprecisely. Predictions between power and state are also inconsistent. A multi-task non-intrusive load disaggregation architecture was proposed to address these limitations. The architecture was based on a transformer and a multi-scale attention convolutional network. Two parallel branches were contained within the architecture. These branches were designated as power disaggregation and state recognition. A transformer structure was used in the power disaggregation branch. A multi-head self-attention mechanism was employed by this structure to capture long-range dependencies and dynamic patterns in the load sequence. In the state recognition branch, a dedicated convolutional neural network was designed. Multi-scale convolutional modules and a channel-spatial attention mechanism were utilized by this network to extract key features of appliance switching events. Inter-task consistency was enhanced through a specific fusion mechanism. Element-wise multiplication was performed between predicted power values and state probabilities. The power output was directly constrained by the use of state information. As a result, appliances were identified in off-states. In these states, power false positives were significantly suppressed. The proposed model was evaluated on the public REDD（reference energy disaggregation dataset） and UK-DALE（UK domestic appliance-level electricity） datasets. Superior performance over existing mainstream methods is achieved. This performance is demonstrated across key evaluation metrics. These metrics are MAE（mean absolute error）, SAE（signal aggregate error）, and the F₁-score.更多还原