Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12358/28502
Title | Office Appliances Identification and Monitoring using Deep Leaning based Energy Disaggregation for Smart Buildings |
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Title in Arabic | تحديد 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. |
Type | Conference Paper |
Date | 2020-10 |
Language | English |
Published in | IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society |
Publisher | IEEE |
Citation | |
Item link | Item Link |
DOI | 10.1109/IECON43393.2020.9255127 |
License | ![]() |
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Files in this item | ||
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accepted paper | 1.193Mb |