Bitte benutzen Sie diese Referenz, um auf diese Ressource zu verweisen:
Volltext verfügbar? / Dokumentlieferung
doi:10.22028/D291-38124
Titel: | Office Appliances Identification and Monitoring using Deep Leaning based Energy Disaggregation for Smart Buildings |
VerfasserIn: | El Astal, Mohammed Taha Kalloub, Mohammed Abu-Hudrouss, Ammar Frey, Georg |
HerausgeberIn: | Zhu, Xing De Silva, Daswin |
Sprache: | Englisch |
Titel: | IECON 2020 - the 46th Annual Conference of the IEEE Industrial Electronics Society : online, Singapore, 19-21 October, 2020 : proceedings |
Seiten: | 1986-1991 |
Verlag/Plattform: | IEEE |
Erscheinungsjahr: | 2020 |
Erscheinungsort: | Piscataway, NJ |
Konferenzort: | Singapore |
Freie Schlagwörter: | Recurrent neural networks Neurons Training Home appliances Monitoring Computer architecture Feature extraction |
DDC-Sachgruppe: | 600 Technik |
Dokumenttyp: | Konferenzbeitrag (in einem Konferenzband / InProceedings erschienener Beitrag) |
Abstract: | Analysis of electrical energy metering profiles has experienced a substantial increase of research activity in recent years. This smart metering is a tool for monitoring energy usage and users' behaviors as a prerequisite for substantial energy savings. Instead of having a sensor at each appliance, non-Intrusive Load Monitoring (NILM) provides a cheaper solution by disaggregating the load data from a single meter using digital signal processing. Different algorithms have been successfully applied to a variety of load scenarios. Load data for small office appliances is available in the BLOND data set (Building-Level Office eNvironment Dataset) such as laptops, computer monitors, etc. The potential energy saving of each small appliance cannot be neglected, particularly in large companies/institutes. In this paper, a recurrent neural network (RNN) with long-short term memory (LSTM) is designed, trained, and validated for NILM on small power office equipment provided in the BLOND data set. A comparison to combinatorial optimization and factorial hidden Markov models using five metrics for performance testing shows good results for the proposed RNN. Index Terms-non-Intrusive Load Monitoring (NILM), recurrent neural networks, energy disaggregation, smart metering, smart buildings. |
DOI der Erstveröffentlichung: | 10.1109/IECON43393.2020.9255127 |
URL der Erstveröffentlichung: | https://ieeexplore.ieee.org/document/9255127 |
Link zu diesem Datensatz: | urn:nbn:de:bsz:291--ds-381248 hdl:20.500.11880/35004 http://dx.doi.org/10.22028/D291-38124 |
ISBN: | 978-1-7281-5414-5 978-1-72815-415-2 |
Datum des Eintrags: | 25-Jan-2023 |
Fakultät: | NT - Naturwissenschaftlich- Technische Fakultät |
Fachrichtung: | NT - Systems Engineering |
Professur: | NT - Prof. Dr. Georg Frey |
Sammlung: | SciDok - Der Wissenschaftsserver der Universität des Saarlandes |
Dateien zu diesem Datensatz:
Es gibt keine Dateien zu dieser Ressource.
Alle Ressourcen in diesem Repository sind urheberrechtlich geschützt.