Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/2429
Title: Generalization Capacity Analysis of Non-Intrusive Load Monitoring using Deep Learning
Authors: Çimen, Halil
Palacios-Garcia, Emilio J.
Çetinkaya, Nurettin
Kolbak, Morten
Sciume, Giuseppe
Vasquez, Juan C.
Guerrero, Josep M.
Keywords: load monitoring
deep learning
energy disaggregation
demand response
energy management
Issue Date: 2020
Publisher: Ieee
Abstract: Appliance Load Monitoring is a technique used to monitor devices existing in homes, industry or naval vessels. Acquisition of device-level data can provide great benefits in many areas such as energy management, demand response, and load forecasting. However, the monitoring process is often provided with a costly installation, as it requires a large number of sensors and a data center. Non-Intrusive Load Monitoring (NILM) is an alternative and cost-efficient load monitoring solution. Simply put, NILM is the process of obtaining device-level data by analyzing the aggregated data read from the main meter that measures the electricity consumption of the whole house. Before NILM analysis is performed, the load patterns of the appliances are usually modeled individually. In general, one model for each appliance is modeled even if the appliance has more than one operating program such as washing machine and oven. Therefore, when the appliance operates in other programs, the accuracy of NILM analysis decreases. In this paper, an appliance-based NILM analysis has been made considering the appliances having multiple operating programs. In order to increase the accuracy of NILM analysis, several deep learning methods, which are the most important data-driven technique of recent times, are used. Developed models were tested in IoT Microgrid Laboratory environment.
Description: 20th IEEE Mediterranean Eletrotechnical Conference (IEEE MELECON) -- JUN 15-18, 2020 -- ELECTR NETWORK
URI: https://hdl.handle.net/20.500.13091/2429
ISBN: 978-1-7281-5200-4
ISSN: 2158-8481
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

Show full item record

CORE Recommender

SCOPUSTM   
Citations

1
checked on Jan 28, 2023

Page view(s)

32
checked on Jan 23, 2023

Google ScholarTM

Check

Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.