商业建筑负荷约占城市终端负荷的25 %，其中包含大量如空调等可灵活调节的负荷，使得商业建筑具有显著的需求响应调节潜力。负荷分解识别可调资源以支持电网制定响应策略，而现有非侵入式分解方法受限于建筑设备运行频次差异，单一时间尺度建模难以在边端设备高效捕捉异步设备特征。为此，提出基于多时间尺度深度学习的非侵入式负荷分解方法。首先，构建了卷积神经网络和长短期记忆神经网络融合模型，实现负荷动态变化中时空特征的协同提取。其次，通过多时间尺度采样机制进行时间敏感性分析，选取边端最佳采样时间。最后，基于商业建筑实际负荷数据的实验表明所提模型在综合性能最优，当采样时间间隔从30 s延长至60 s时，所提模型的马修斯相关系数提升了7.35 %。

Commercial building loads account for approximately 25 % of the total end-use load in urban areas, with a substantial portion comprising flexible loads such as air conditioning systems. This endows commercial buildings with significant potential for demand response （DR） regulation. Load disaggregation serves as a means of identifying flexible resources to facilitate the formulation of demand response strategies within the power grid. Nevertheless, existing non-intrusive disaggregation approaches are constrained by variations in the operating frequencies of building systems, whereby single time-scale models exhibit limited capability in effectively capturing the asynchronous characteristics of end-use devices. To overcome these limitations, a non-intrusive load disaggregation method based on multi-timescale deep learning is proposed in this paper. Specifically, a hybrid model integrating convolutional neural networks and long short-term memory networks is constructed to enable the collaborative extraction of spatiotemporal features from dynamic load data. Furthermore, a multi-timescale sampling mechanism is employed to conduct temporal sensitivity analysis and to determine the optimal sampling interval at the edge. Finally, experimental results using real-world commercial building load data demonstrate that the proposed model achieves superior comprehensive performance. When the sampling interval is increased from 30 seconds to 60 seconds, the Matthews correlation coefficient of the proposed model is improved by 7.35 %.