Great concern regarding energy efficiency has led the research community to develop approaches which enhance the energy awareness by means of insightful representations. An example of intuitive energy representation is the parts-based representation provided by Non-Intrusive Load Monitoring (NILM) techniques which decompose non-measured individual loads from a single total measurement of the installation, resulting in more detailed information about how the energy is spent along the electrical system. Although there are previous works that have achieved important results on NILM, the majority of the NILM systems were only validated in residential buildings, leaving a niche for the study of energy disaggregation in non-residential buildings, which present a specific behavior. In this article, we suggest a novel fully-convolutional denoising auto-encoder architecture (FCN-dAE) as a convenient NILM system for large non-residential buildings, and it is compared, in terms of particular aspects of large buildings, to previous denoising auto-encoder approaches (dAE) using real electrical consumption from a hospital facility. Furthermore, by means of three use cases, we show that our approach provides extra helpful funcionalities for energy management tasks in large buildings, such as meter replacement, gap filling or novelty detection.

中文翻译：

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大型非住宅建筑物中用于NILM的全卷积去噪自动编码器

对能源效率的高度关注已导致研究界开发出通过有见地的表述来增强能源意识的方法。直观的能源表示方式的一个示例是非侵入式负载监控（NILM）技术提供的基于零件的表示方式，该技术可从一次安装总测量中分解出未测量的单个负荷，从而获得有关如何使用能源的更详细的信息沿电气系统。尽管以前有一些在NILM上取得重要成果的工作，但是大多数NILM系统仅在住宅建筑中得到验证，这为研究非住宅建筑中的能量分布问题提供了一个利基。在本文中，我们建议一种新颖的全卷积去噪自动编码器体系结构（FCN-dAE）作为用于大型非住宅建筑物的便捷NILM系统，并就大型建筑物的特定方面将其与以前的去噪自动编码器方法进行比较（dAE）使用医院设施的实际耗电量。此外，通过三个用例，我们证明了我们的方法为大型建筑物中的能源管理任务提供了额外的有用功能，例如仪表更换，间隙填充或新颖性检测。