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Effects of Predictors on Power Consumption Estimation for IT Rack in a Data Center: An Experimental Analysis

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  • Mehmet Türker Takcı

    (Department of Electronics Engineering, Gebze Technical University, Kocaeli 41400, Turkey)

  • Tuba Gözel

    (Department of Electronics Engineering, Gebze Technical University, Kocaeli 41400, Turkey)

Abstract

The appropriate feature/predictor selection is as significant as building efficient estimation methods for the accurate estimation of power consumption, which is required for self-awareness and autonomous decision systems. Traditional methodologies define predictors by assessing whether there is a relationship between the predictors and the response variable. Contrarily, this study determines predictors based on their individual and group impacts on the estimation accuracy directly. To analyze the impact of predictors on the power-consumption estimation of an IT rack in a data center, estimations were carried out employing each prospective predictor separately using the measured data under the real-world workload. Then, the ratio of CPU usage was set as the default predictor, and the remaining variables were assigned as the second predictor one by one. By utilizing the same approach, the best combination of predictors was determined. As a result, it was discovered that some variables with a low correlation coefficient with power consumption improved the estimation accuracy, whereas some variables with high correlation coefficients worsened the estimation result. The CPU is the most power-consuming component in the server and one of the most used predictors in the literature. However, the estimation accuracy obtained using only the CPU is 10 times worse than the estimation result conducted by utilizing the predictor set determined at the end of the experiments. This study shows that instead of choosing predictors only from one point of view or one method, it is more convenient to select predictors by assessing their influence on estimation results. Examining the trend and characteristics of the estimated variable should also be considered.

Suggested Citation

  • Mehmet Türker Takcı & Tuba Gözel, 2022. "Effects of Predictors on Power Consumption Estimation for IT Rack in a Data Center: An Experimental Analysis," Sustainability, MDPI, vol. 14(21), pages 1-19, November.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14663-:d:965990
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    References listed on IDEAS

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    1. Xin Gao & Xiaobing Li & Bing Zhao & Weijia Ji & Xiao Jing & Yang He, 2019. "Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature Selection," Energies, MDPI, vol. 12(6), pages 1-18, March.
    2. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2018. "Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †," Energies, MDPI, vol. 11(7), pages 1-20, June.
    3. Songyao Wang & Zhisheng Zhang, 2021. "Short-Term Multiple Load Forecasting Model of Regional Integrated Energy System Based on QWGRU-MTL," Energies, MDPI, vol. 14(20), pages 1-13, October.
    4. Adnan Yousaf & Rao Muhammad Asif & Mustafa Shakir & Ateeq Ur Rehman & Mohmmed S. Adrees, 2021. "An Improved Residential Electricity Load Forecasting Using a Machine-Learning-Based Feature Selection Approach and a Proposed Integration Strategy," Sustainability, MDPI, vol. 13(11), pages 1-20, May.
    5. Shaoqian Pei & Hui Qin & Liqiang Yao & Yongqi Liu & Chao Wang & Jianzhong Zhou, 2020. "Multi-Step Ahead Short-Term Load Forecasting Using Hybrid Feature Selection and Improved Long Short-Term Memory Network," Energies, MDPI, vol. 13(16), pages 1-23, August.
    6. Jin, Chaoqiang & Bai, Xuelian & Yang, Chao & Mao, Wangxin & Xu, Xin, 2020. "A review of power consumption models of servers in data centers," Applied Energy, Elsevier, vol. 265(C).
    7. Fridgen, Gilbert & Keller, Robert & Thimmel, Markus & Wederhake, Lars, 2017. "Shifting load through space–The economics of spatial demand side management using distributed data centers," Energy Policy, Elsevier, vol. 109(C), pages 400-413.
    8. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    9. Lintao Yang & Honggeng Yang & Haitao Liu, 2018. "GMDH-Based Semi-Supervised Feature Selection for Electricity Load Classification Forecasting," Sustainability, MDPI, vol. 10(1), pages 1-16, January.
    10. Lin Lin & Lin Xue & Zhiqiang Hu & Nantian Huang, 2018. "Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours," Energies, MDPI, vol. 11(7), pages 1-30, July.
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