A novel traffic prediction method using machine learning for energy efficiency in service provider networks

Rau, Francisco; Soto, Ismael; Zabala-Blanco, David; Azurdia-Meza, Cesar A.; Ijaz, Muhammad; Ekpo, Sunday; Gutierrez, Sebastian

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dc.contributor.author	Rau, Francisco
dc.contributor.author	Soto, Ismael
dc.contributor.author	Zabala-Blanco, David
dc.contributor.author	Azurdia-Meza, Cesar A.
dc.contributor.author	Ijaz, Muhammad
dc.contributor.author	Ekpo, Sunday
dc.contributor.author	Gutierrez, Sebastian
dc.date.accessioned	2023-07-10T19:56:57Z
dc.date.available	2023-07-10T19:56:57Z
dc.date.issued	2023
dc.identifier.uri	http://repositorio.ucm.cl/handle/ucm/4870
dc.description.abstract	This paper presents a systematic approach for solving complex prediction problems with a focus on energy efficiency. The approach involves using neural networks, specifically recurrent and sequential networks, as the main tool for prediction. In order to test the methodology, a case study was conducted in the telecommunications industry to address the problem of energy efficiency in data centers. The case study involved comparing four recurrent and sequential neural networks, including recurrent neural networks (RNNs), long short-term memory (LSTM), gated recurrent units (GRUs), and online sequential extreme learning machine (OS-ELM), to determine the best network in terms of prediction accuracy and computational time. The results show that OS-ELM outperformed the other networks in both accuracy and computational efficiency. The simulation was applied to real traffic data and showed potential energy savings of up to 12.2% in a single day. This highlights the importance of energy efficiency and the potential for the methodology to be applied to other industries. The methodology can be further developed as technology and data continue to advance, making it a promising solution for a wide range of prediction problems.	es_CL
dc.language.iso	en	es_CL
dc.rights	Atribución-NoComercial-SinDerivadas 3.0 Chile	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/cl/	*
dc.source	Sensors, 23(11), 4997	es_CL
dc.subject	Energy efficiency	es_CL
dc.subject	Machine learning	es_CL
dc.subject	Telecom services operator	es_CL
dc.subject	Traffic prediction	es_CL
dc.title	A novel traffic prediction method using machine learning for energy efficiency in service provider networks	es_CL
dc.type	Article	es_CL
dc.ucm.facultad	Facultad de Ciencias de la Ingeniería	es_CL
dc.ucm.indexacion	Scopus	es_CL
dc.ucm.indexacion	Isi	es_CL
dc.ucm.uri	mdpi.com/1424-8220/23/11/4997	es_CL
dc.ucm.doi	doi.org/10.3390/s23114997	es_CL