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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

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