• العربية
    • English
  • English 
    • العربية
    • English
  • Login
Home
Publisher PoliciesTerms of InterestHelp Videos
Submit Thesis
IntroductionIUGSpace Policies
JavaScript is disabled for your browser. Some features of this site may not work without it.
View Item 
  •   Home
  • Faculty of Engineering
  • Staff Publications- Faculty of Engineering
  • View Item
  •   Home
  • Faculty of Engineering
  • Staff Publications- Faculty of Engineering
  • View Item

Please use this identifier to cite or link to this item:

http://hdl.handle.net/20.500.12358/28502
TitleOffice Appliances Identification and Monitoring using Deep Leaning based Energy Disaggregation for Smart Buildings
Title in Arabicتحديد الأجهزة المكتبية ومراقبتها باستخدام تصنيف الطاقة القائم على التعلم العميق في المباني الذكية
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 pre-requisite 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.

Authors
El Astal, Mohammed
Abu-Hudrouss, Ammar M.
Frey, Georg
Kalloub, Mohammed
TypeConference Paper
Date2020-10
LanguageEnglish
Subjects
non-Intrusive Load Monitoring (NILM)
recurrent neural networks
energy disaggregation
smart metering
smart buildings
Published inIECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE
Citation
Item linkItem Link
DOI10.1109/IECON43393.2020.9255127
License
Collections
  • Staff Publications- Faculty of Engineering [908]
Files in this item
accepted paper1.193Mb

The institutional repository of the Islamic University of Gaza was established as part of the ROMOR project that has been co-funded with support from the European Commission under the ERASMUS + European programme. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

Contact Us | Send Feedback
 

 

Browse

All of IUGSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsSupervisorsThis CollectionBy Issue DateAuthorsTitlesSubjectsSupervisors

My Account

LoginRegister

Statistics

View Usage Statistics

The institutional repository of the Islamic University of Gaza was established as part of the ROMOR project that has been co-funded with support from the European Commission under the ERASMUS + European programme. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

Contact Us | Send Feedback