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An Efficient Deep Learning Framework for Intelligent Energy Management in IoT Networks

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An Efficient Deep Learning Framework for Intelligent Energy Management in IoT Networks

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Han, T.; Muhammad, K.; Hussain, T.; Lloret, J.; Baik, SW. (2021). An Efficient Deep Learning Framework for Intelligent Energy Management in IoT Networks. IEEE Internet of Things. 8(5):3170-3179. https://doi.org/10.1109/JIOT.2020.3013306

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/188320

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Title: An Efficient Deep Learning Framework for Intelligent Energy Management in IoT Networks
Author: Han, Tao Muhammad, Khan Hussain, Tanveer Lloret, Jaime Baik, Sung Wook
UPV Unit: Universitat Politècnica de València. Departamento de Comunicaciones - Departament de Comunicacions
Issued date:
Abstract:
[EN] Green energy management is an economical solution for better energy usage, but the employed literature lacks focusing on the potentials of edge intelligence in controllable Internet of Things (IoT). Therefore, in this ...[+]
Subjects: Forecasting , Energy management , Smart grids , Load forecasting , Machine learning , Internet of Things , Servers , Dependable Internet of Things (IoT) , Edge computing , Energy forecasting , GRU , Long short-term memory (LSTM) , Smart homes , Industries
Copyrigths: Reserva de todos los derechos
Source:
IEEE Internet of Things. (eissn: 2327-4662 )
DOI: 10.1109/JIOT.2020.3013306
Publisher:
Institute of Electrical and Electronics Engineers
Publisher version: https://doi.org/10.1109/JIOT.2020.3013306
Project ID:
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-84802-C2-1-P/ES/RED COGNITIVA DEFINIDA POR SOFTWARE PARA OPTIMIZAR Y SECURIZAR TRAFICO DE INTERNET DE LAS COSAS CON INFORMACION CRITICA/
info:eu-repo/grantAgreement/NRF//2019M3F2A1073179/
info:eu-repo/grantAgreement/EC/FP7/609475/EU
Thanks:
This work was supported in part by the National Research Foundation of Korea Grant Funded by the Korea Government (MSIT) under Grant 2019M3F2A1073179; in part by the "Ministerio de Economia y Competitividad" in the "Programa ...[+]
Type: Artículo

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