Detection and Classification of Refrigeration Units in a Commercial Environment: Comparing Neural Networks to Unsupervised Clustering
| dc.contributor.author | Van Zaen, Jérôme. | |
| dc.contributor.author | Moufawad El Achkar, Christopher. | |
| dc.contributor.author | Carrillo, Rafael E. | |
| dc.contributor.author | Hutter, Andreas. | |
| dc.date.accessioned | 2021-12-09T14:02:02Z | |
| dc.date.available | 2021-12-09T14:02:02Z | |
| dc.date.issued | 2018 | |
| dc.description.abstract | Non-intrusive load monitoring aims to estimate the power consumption of individual appliances from a single meter. Several methods have been proposed to solve this blind source separation problem, such as clustering, hidden Markov models, or neural networks. We present two approaches for detecting and classifying refrigeration units in a commercial environment. The first one is based on unsupervised event detection and the second one on neural networks with convolutional layers. We show that both approaches can accurately recognize power cycles of refrigeration units. The extracted cycles can then be used to reduce total energy consumption or for predictive maintenance by identifying units with an increased risk of failure. | |
| dc.identifier.citation | NILM2018 Proceedings, March 7-8, 2018 | Austin, Texas | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12839/280 | |
| dc.identifier.url | http://nilmworkshop.org/2018/proceedings/Poster_ID20.pdf | |
| dc.subject | NILM, unsupervised event detection, convolutional neuronal networks, deep learning, artificial intelligence, energy management, predictive maintenance | |
| dc.title | Detection and Classification of Refrigeration Units in a Commercial Environment: Comparing Neural Networks to Unsupervised Clustering | |
| dc.type | Proceedings Article | |
| dc.type.csemdivisions | BU-V | |
| dc.type.csemresearchareas | Digital Energy |