In this paper, a novel mask-based load disaggregation scheme is presented to extract flexible load profiles in buildings. Flexible loads are those that can be adjusted as needed and examples of such loads are heating, ventilation, and air-conditioning (HVAC), and lighting loads. Knowledge about the flexible sub-load in buildings is crucial for demand-side management programs. However, the load decomposition performance of conventional disaggregation methods may be limited mainly because similar profiles are superimposed on the entire load of the buildings. Motivated by these problems, a deep neural network (DNN)-based mask, termed DNNM, is proposed. It is customized in a time–frequency (T-F) domain to effectively extract the flexible portion of loads. To the best of our knowledge, the DNNM is to achieve load disaggregation using the distinctive T-F properties of the flexible loads. Particularly, a new mask is designed to increase the load disaggregation accuracy by determining the appropriate ratio of flexible load in the mixed loads, adaptively for different T-F elements. Numerical evaluations for residential and commercial building loads show that the proposed DNNM scheme outperforms the conventional disaggregation models in discriminating the contributions of the flexible load from the total power consumption.

中文翻译：

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基于深度神经网络的楼宇柔性荷载分解时频掩码估计

在本文中，提出了一种新的基于掩码的负载分解方案来提取建筑物中的灵活负载分布。灵活负载是指可以根据需要进行调整的负载，此类负载的示例包括供暖、通风和空调 (HVAC) 以及照明负载。有关建筑物中灵活子负载的知识对于需求侧管理计划至关重要。然而，传统分解方法的荷载分解性能可能受到限制，主要是因为相似的剖面叠加在建筑物的整个荷载上。受这些问题的启发，提出了一种基于深度神经网络 (DNN) 的掩码，称为 DNNM。它在时频 (TF) 域中进行了定制，以有效地提取负载的灵活部分。据我们所知，DNNM 将使用灵活负载的独特 TF 特性实现负载分解。特别是，设计了一种新的掩码，通过确定混合载荷中灵活载荷的适当比例，自适应地针对不同的 TF 元素来提高载荷分解精度。住宅和商业建筑负载的数值评估表明，所提出的 DNNM 方案在区分灵活负载对总功耗的贡献方面优于传统的分解模型。