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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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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,就预测客户行为或IT公司所需的任何数据而言,估计算法变得越来越流行。预测结果可用于不同目的,例如提高生产和服务的质量和容量,减少温室气体排放并最大程度地减少能耗。准确的预测结果也有利于数据中心,这些数据中心在电力市场中是消耗大量电力需求的重要参与者,并且有机会通过将其灵活的负荷重新安排到未来期间来减少消耗的电力,电力成本。在本文中,将解释功耗设备和影响功耗的变量。此外,还提供了有关人工神经网络和回归分析方法的简要信息。信息技术设备的功耗通过非线性回归分析和人工神经网络方法进行预测。预测结果表明,人工神经网络方法更为成功。

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