Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Information Engineering and Science
Elizabeth Thomas
Unknown Affiliation
Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Optimizing Energy Consumption in Data Centers Using Machine Learning-Based Predictive Analytics James Wilson; Patricia Taylor; Elizabeth Thomas
International Journal of Information Engineering and Science Vol. 1 No. 1 (2024): February : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v1i1.51

Abstract

Data centers are major contributors to global energy consumption, with significant implications for operational costs and environmental sustainability. As energy demand increases, optimizing energy usage within these facilities has become essential. This study investigates the application of machine learning-based predictive analytics to enhance energy efficiency in data centers. By leveraging models such as Random Forest, Neural Networks, and Deep Learning, predictive analytics forecasts energy demands based on variables like temperature, workload, and time of day. Results from multiple case studies reveal that machine learning models can reduce energy consumption by up to 20%, offering a sustainable solution without compromising data center performance.
Co-Authors James Wilson Patricia Taylor