10 Aralık 2019 Konferans bildirisi Açık Erişim

Takci Mehmet Turker; Gözel Tuba; Hocaoglu Mehmet Hakan

Dublin Core

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<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Takci Mehmet Turker</dc:creator>
  <dc:creator>Gözel Tuba</dc:creator>
  <dc:creator>Hocaoglu Mehmet Hakan</dc:creator>
  <dc:date>2019-12-10</dc:date>
  <dc:description>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.</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/252117</dc:identifier>
  <dc:identifier>oai:aperta.ulakbim.gov.tr:252117</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by-sa</dc:rights>
  <dc:subject>Regression Analysis</dc:subject>
  <dc:subject>Artificial Neural Network</dc:subject>
  <dc:subject>Power Consumption Forecasting</dc:subject>
  <dc:subject>Data Center</dc:subject>
  <dc:title>Forecasting Power Consumption of IT Devices in a Data Center</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
  <dc:type>publication-conferencepaper</dc:type>
</oai_dc:dc>