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dc.contributor | Escuela de Ingenierias Industrial e Informatica | es_ES |
dc.contributor.author | Alonso Castro, Serafín | |
dc.contributor.author | Morán Palao, Antonio | |
dc.contributor.author | Pérez López, Daniel | |
dc.contributor.author | Reguera Acevedo, Perfecto | |
dc.contributor.author | Díaz, Ignacio | |
dc.contributor.author | Domínguez González, Manuel | |
dc.contributor.other | Ingenieria de Sistemas y Automatica | es_ES |
dc.date | 2017 | |
dc.date.accessioned | 2019-11-04T17:59:33Z | |
dc.date.available | 2019-11-04T17:59:33Z | |
dc.identifier.citation | International Conference on Engineering Applications of Neural Networks, 2017 | es_ES |
dc.identifier.issn | 1865-0929 | |
dc.identifier.uri | http://hdl.handle.net/10612/11292 | |
dc.description | P. 307-319 | es_ES |
dc.description.abstract | Intensive use of heating, ventilation and air conditioning (HVAC) systems in buildings entails an analysis and monitoring of their e ciency. Cooling systems are key facilities in large buildings, and par- ticularly critical in hospitals, where chilled water production is needed as an auxiliary resource for a large number of devices. A chiller plant is often composed of several HVAC units running at the same time, be- ing impossible to assess the individual cooling production and e ciency, since a sensor is seldom installed due to the high cost. We propose a virtual sensor that provides an estimation of the cooling production, based on a deep learning architecture that features a 2D CNN (Convolu- tional Neural Network) to capture relevant features on two-way matrix arrangements of chiller data involving thermodynamic variables and the refrigeration circuits of the chiller unit. Our approach has been tested on an air-cooled chiller in the chiller plant at a hospital, and compared to other state-of-the-art methods using 10-fold cross-validation. Our re- sults report the lowest errors among the tested methods and include a comparison of the true and estimated cooling production and e ciency for a period of several days | es_ES |
dc.language | eng | es_ES |
dc.publisher | Springer | es_ES |
dc.subject | Ingeniería de sistemas | es_ES |
dc.subject.other | Virtual sensor | es_ES |
dc.subject.other | HVAC systems | es_ES |
dc.subject.other | Cooling power | es_ES |
dc.subject.other | Eficiencia | es_ES |
dc.subject.other | Deep Learning | es_ES |
dc.subject.other | Convolutional Neural Network | es_ES |
dc.title | Virtual Sensor Based on a Deep Learning Approach for Estimating Efficiency in Chillers | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.identifier.doi | 10.1007/978-3-030-20257-6_26 | |
dc.description.peerreviewed | SI | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.identifier.essn | 1865-0937 | |
dc.volume.number | 1000 | es_ES |
dc.page.initial | 307 | es_ES |
dc.page.final | 319 | es_ES |
dc.type.hasVersion | info:eu-repo/semantics/acceptedVersion | es_ES |
dc.subject.unesco | automática | es_ES |
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