Forecasting Power Consumption of IT Devices in a Data Center

Mehmet Türker Takcı, T. Gözel, M. H. Hocaoğlu
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引用次数: 2

Abstract

In recent years, estimation algorithms become more popular in terms of forecasting customer behavior or any required data for IT companies. Forecasting results can be used in different purposes such as improving the quality and capacity of production and services, reducing to greenhouse gas emissions, and minimizing the power consumption. The accurate forecasting results are also beneficial for data centers which are the significant participants in the electricity market in terms of consuming huge power demand and have a chance to reduce consumed power, electricity costs by rescheduling their flexible loads for the future period. In this paper, power-consuming devices and variables affecting power consumption are explained. Also, the brief information about artificial neural network and regression analysis methods has been provided. The power consumption of Information Technology devices is forecasted by nonlinear regression analysis and artificial neural network methods. The forecasting results show that artificial neural network method is more successful.
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数据中心IT设备功耗预测
近年来,估计算法在预测客户行为或IT公司所需的任何数据方面变得越来越流行。预测结果可用于不同的目的,如提高生产和服务的质量和能力,减少温室气体排放,并尽量减少电力消耗。准确的预测结果也有利于数据中心作为电力市场的重要参与者,在消耗巨大的电力需求方面,并有机会通过在未来一段时间内重新安排其灵活的负载来降低消耗的电力,电力成本。本文对功耗器件和影响功耗的变量进行了说明。同时,简要介绍了人工神经网络和回归分析方法。采用非线性回归分析和人工神经网络方法对信息技术设备的功耗进行了预测。预测结果表明,人工神经网络方法较为成功。
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