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dc.contributor.author | Stinner, Florian![]() |
es_ES |
dc.contributor.author | Llopis-Mengual, Belén![]() |
es_ES |
dc.contributor.author | Storek, Thomas![]() |
es_ES |
dc.contributor.author | Kümpel, Alexander![]() |
es_ES |
dc.contributor.author | Müller, Dirk![]() |
es_ES |
dc.date.accessioned | 2022-11-22T19:02:52Z | |
dc.date.available | 2022-11-22T19:02:52Z | |
dc.date.issued | 2022-06 | es_ES |
dc.identifier.issn | 0926-5805 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/190054 | |
dc.description.abstract | [EN] Optimizing the operation of building energy systems holds great potential to reduce energy consumption in buildings. However, this requires detailed system information, such as the relationship of sensor data. Automatic detection of this information requires monitoring data from buildings, which is rarely available in the needed quality for automatic assignment. This study bases on 200 weeks of data collected from eight temperature sensors of a heat pump and a heat exchanger in 5-min samples. We use this data to auto-generate grey-box models to extend the data set with 500 weeks of simulated data. We train six supervised deep learning algorithms with all the data to test whether detecting connections is possible. The maximum F1 score of 94.9% compared to realbased results with a maximum of 34.2%, which is over 60% better. The advantage of the proposed approach is its independence from the low availability of real data. | es_ES |
dc.description.sponsorship | We gratefully acknowledge the financial support provided by the BMWi (Federal Ministry for Economic Affairs and Energy) with promotional reference 03SBE006A. B. Llopis-Mengual acknowledges the Ministry of Universities of Spain through the "Formacion de Profesorado Universitario" programme ref. FPU 19/04012. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Automation in Construction | es_ES |
dc.rights | Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) | es_ES |
dc.subject | Model-based time series generation | es_ES |
dc.subject | Topology detection | es_ES |
dc.subject | Building energy systems | es_ES |
dc.subject | Relation inference | es_ES |
dc.subject | Building automation | es_ES |
dc.subject | Supervised learning | es_ES |
dc.title | Comparative study of supervised algorithms for topology detection of sensor networks in building energy systems | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.autcon.2022.104248 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/ //FPU19%2F04012//AYUDA PREDOCTORAL FPU-LLOPIS MENGUAL. PROYECTO: CARACTERIZACIÓN DE LAS PRESTACIONES DE MÁQUINAS FRIGORÍFICAS Y BOMBAS DE CALOR Y ANÁLISIS DE LAS INFLUENCIAS DE LAS CONDICIONES DE OPERACIÓN DE LAS MISMAS/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/BMWI//03SBE006A/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.description.bibliographicCitation | Stinner, F.; Llopis-Mengual, B.; Storek, T.; Kümpel, A.; Müller, D. (2022). Comparative study of supervised algorithms for topology detection of sensor networks in building energy systems. Automation in Construction. 138:1-15. https://doi.org/10.1016/j.autcon.2022.104248 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.autcon.2022.104248 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 15 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 138 | es_ES |
dc.relation.pasarela | S\462934 | es_ES |
dc.contributor.funder | MINISTERIO DE UNIVERSIDADES E INVESTIGACION | es_ES |
dc.contributor.funder | Bundesministerium für Wirtschaft und Energie, Alemania | es_ES |