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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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A Novel Traffic Prediction Method Using Machine Learning for Energy Efficiency in Service Provider Networks.

Francisco Rau1, Ismael Soto1, David Zabala-Blanco2

  • 1CIMTT, Department of Electrical Engineering, Universidad de Santiago de Chile, Santiago 9170124, Chile.

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This study introduces a neural network approach for energy efficiency predictions. Online Sequential Extreme Learning Machine (OS-ELM) demonstrated superior accuracy and efficiency in data center energy management.

Keywords:
energy efficiencymachine learningtelecom services operatortraffic prediction

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Energy Systems

Background:

  • Data centers face increasing energy demands, necessitating efficient operational strategies.
  • Accurate prediction of energy consumption is crucial for optimizing resource allocation and reducing environmental impact.
  • Traditional prediction methods may not adequately capture the dynamic nature of energy usage in complex systems.

Purpose of the Study:

  • To present a systematic methodology for complex prediction problems, emphasizing energy efficiency.
  • To evaluate the performance of various recurrent and sequential neural networks for data center energy prediction.
  • To identify the most accurate and computationally efficient neural network model for energy efficiency applications.

Main Methods:

  • Utilized recurrent and sequential neural networks for prediction tasks.
  • Conducted a case study in the telecommunications industry focusing on data center energy efficiency.
  • Compared four neural network models: Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), and Online Sequential Extreme Learning Machine (OS-ELM).
  • Evaluated models based on prediction accuracy and computational time using real traffic data.

Main Results:

  • The Online Sequential Extreme Learning Machine (OS-ELM) model achieved superior prediction accuracy compared to RNNs, LSTM, and GRUs.
  • OS-ELM also demonstrated higher computational efficiency, indicating faster processing times.
  • Simulations using real traffic data projected potential daily energy savings of up to 12.2%.

Conclusions:

  • The proposed systematic approach, particularly using OS-ELM, is highly effective for energy efficiency prediction in data centers.
  • The methodology shows significant potential for reducing energy consumption and can be adapted to other industries.
  • Further advancements in technology and data will enhance the applicability of this approach for diverse prediction challenges.