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Semisupervised refrigeration plant cooling disaggregation by means of deep neural network ensemble

Author
Cirera, J.; Cariño, J. A.; Zurita, D.; Ortega, J.A.
Type of activity
Presentation of work at congresses
Name of edition
28th IEEE International Symposium on Industrial Electronics
Date of publication
2019
Presentation's date
2019-06-12
Book of congress proceedings
2019 IEEE 28th International Symposium on Industrial Electronics: proceedings: Vancouver, BC, Canada: 12-14 June, 2019
First page
1761
Last page
1766
DOI
10.1109/ISIE.2019.8781335
Repository
http://hdl.handle.net/2117/170734 Open in new window
URL
https://ieeexplore.ieee.org/document/8781335 Open in new window
Abstract
The awareness of the energy usage has become a recurrent topic during the last decades. Identifying the end-use energy of each individual device can lead to a substantial improvement in efficiency and fault detection. The cost of instrumentation and especially the ones which involve fluids, makes the monitoring unfeasible. Hereby, the necessity of Non-Intrusive Load Monitoring (NILM) techniques has increased in order to avoid the aforementioned associated costs. In this paper, the cooling power ...
Keywords
Artificial intelligence, Cooling, Load modeling, Multi-layer neural network, Semisupervised learning
Group of research
MCIA - Motion Control and Industrial Applications Research Group
PERC-UPC - Power Electronics Research Centre

Participants