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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; Ijaz, Muhammad; Ekpo, Sunday; Gutierrez, Sebastian.
Afiliação
  • Rau F; CIMTT, Department of Electrical Engineering, Universidad de Santiago de Chile, Santiago 9170124, Chile.
  • Soto I; CIMTT, Department of Electrical Engineering, Universidad de Santiago de Chile, Santiago 9170124, Chile.
  • Zabala-Blanco D; Department of Computer Science and Industry, Universidad Católica del Maule, Talca 3480112, Chile.
  • Azurdia-Meza C; Department of Electrical Engineering, Universidad de Chile, Santiago 8370451, Chile.
  • Ijaz M; Department of Engineering, Faculty of Science and Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK.
  • Ekpo S; Department of Engineering, Faculty of Science and Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK.
  • Gutierrez S; Faculty of Engineering, Universidad Autónoma de Chile, Santiago 7500912, Chile.
Sensors (Basel) ; 23(11)2023 May 23.
Article em En | MEDLINE | ID: mdl-37299722
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Redes Neurais de Computação / Conservação de Recursos Energéticos Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Chile País de publicação: Suíça
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Redes Neurais de Computação / Conservação de Recursos Energéticos Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sensors (Basel) Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Chile País de publicação: Suíça