UNet-NILM: A deep neural network for multi-tasks appliances state detection and power estimation in NILM
| dc.contributor.author | Faustine, Anthony | |
| dc.contributor.author | Pereira, Lucas | |
| dc.contributor.author | Bousbiat, Hafsa | |
| dc.contributor.author | Kulkarni, Shridhar | |
| dc.date.accessioned | 2021-05-06T09:57:17Z | |
| dc.date.available | 2021-05-06T09:57:17Z | |
| dc.date.issued | 2020 | |
| dc.description | Abstract. Full-text article available at: https://doi.org/10.1145/3427771.3427859 | en_US |
| dc.description.abstract | Over the years, an enormous amount of research has been exploring Deep Neural Networks (DNN), particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for estimating the energy consumption of appliances from a single point source such as smart meters - Non-Intrusive Load Monitoring (NILM). However, most of the existing DNNs models for NILM use a single-task learning approach in which a neural network is trained exclusively for each appliance. This strategy is computationally expensive and ignores the fact that multiple appliances can be active simultaneously and dependencies between them. In this work, we propose UNet-NILM for multi-task appliances' state detection and power estimation, applying a multi-label learning strategy and multi-target quantile regression. The UNet-NILM is a one-dimensional CNN based on the U-Net architecture initially proposed for image segmentation. Empirical evaluation on the UK-DALE dataset suggests promising performance against traditional single-task learning. | en_US |
| dc.identifier.citation | Faustine, A., Pereira, L., Bousbiat, H., & Kulkarni, S. (2020). UNet-NILM: UNet-NILM: A deep neural network for multi-tasks appliances state detection and power estimation in NILM. In Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring (pp. 84-88). | en_US |
| dc.identifier.other | DOI: https://doi.org/10.1145/3427771.3427859 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12661/2965 | |
| dc.language.iso | en | en_US |
| dc.publisher | Association for Computing Machinery | en_US |
| dc.subject | Deep Neural Networks | en_US |
| dc.subject | DNN | en_US |
| dc.subject | Convolutional Neural Networks | en_US |
| dc.subject | CNN | en_US |
| dc.subject | Recurrent Neural Networks | en_US |
| dc.subject | RNN | en_US |
| dc.subject | Power estimation | en_US |
| dc.subject | Non-Intrusive Load Monitoring | en_US |
| dc.subject | NILM | en_US |
| dc.subject | Energy consumption | en_US |
| dc.title | UNet-NILM: A deep neural network for multi-tasks appliances state detection and power estimation in NILM | en_US |
| dc.type | Article | en_US |