Fully-Convolutional Denoising Auto-Encoders for NILM in Large Non-Residential Buildings
| Autor: | Daniel Perez-Lopez, Ana Gonzalez-Muniz, Abel Alberto Cuadrado Vega, Ignacio Diaz-Blanco, Manuel Domínguez-González, Diego Garcia-Perez |
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| Rok vydání: | 2021 |
| Předmět: |
General Computer Science
Energy management Computer science 020209 energy 02 engineering and technology computer.software_genre Novelty detection Electric power system 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Use case Data mining Architecture Representation (mathematics) computer Energy (signal processing) Efficient energy use |
| Zdroj: | IEEE Transactions on Smart Grid. 12:2722-2731 |
| ISSN: | 1949-3061 1949-3053 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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