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Forecasting Power Consumption of IT Devices in a Data Center

机译:预测数据中心IT设备的功耗

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摘要

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.
机译:近年来,估计算法在预测客户行为或IT公司任何所需数据方面变得更加流行。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,并最大限度地减少功耗。准确的预测结果对数据中心来说也有利于电力市场的重要参与者在消费巨大的电力需求方面,并且有机会通过重新安排未来期间的灵活负载来减少消耗的电力,电力成本。在本文中,解释了影响功耗的消耗设备和变量。此外,已经提供了关于人工神经网络和回归分析方法的简要信息。非线性回归分析和人工神经网络方法预测信息技术设备的功耗。预测结果表明,人工神经网络方法更成功。

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