Fully-Convolutional Denoising Auto-Encoders for NILM in Large Non-Residential Buildings

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.