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1. Khan, Md Riaz Ahmed. Data Center Load Forecast Using Dependent Mixture Model.

Degree: MS, Electrical Engineering and Computer Science, 2016, South Dakota State University

The dependency on cloud computing is increasing day by day. With the boom of data centers, the cost is also increasing, which forces industries to come up with techniques and methodologies to reduce the data center energy use. Load forecasting plays a vital role in both efficient scheduling and operating a data center as a virtual power plant. In this thesis work a stochastic method, based on dependent mixtures is developed to model the data center load and is used for day-ahead forecast. The method is validated using three data sets from National Renewable Energy Laboratory (NREL) and one other data centers. The proposed method proved better than the classical autoregressive, moving-average, as well as the neural network-based forecasting method, and resulted in a reduction of 7.91% mean absolute percentage error (MAPE) for the forecast. A more accurate forecast can improve power scheduling and resource management reducing the variable cost of power generation as well as the overall data center operating cost, which was quantified as a yearly savings of $13,705 for a typical 100 MW coal fired tier-IV data center. Advisors/Committee Members: Reinaldo Tonkoski, Semhar Michael.

Subjects/Keywords: data center; load forcast; mixture model; Computer Engineering; Data Storage Systems; Electrical and Computer Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Khan, M. R. A. (2016). Data Center Load Forecast Using Dependent Mixture Model. (Masters Thesis). South Dakota State University. Retrieved from http://openprairie.sdstate.edu/etd/1120

Chicago Manual of Style (16th Edition):

Khan, Md Riaz Ahmed. “Data Center Load Forecast Using Dependent Mixture Model.” 2016. Masters Thesis, South Dakota State University. Accessed August 08, 2020. http://openprairie.sdstate.edu/etd/1120.

MLA Handbook (7th Edition):

Khan, Md Riaz Ahmed. “Data Center Load Forecast Using Dependent Mixture Model.” 2016. Web. 08 Aug 2020.

Vancouver:

Khan MRA. Data Center Load Forecast Using Dependent Mixture Model. [Internet] [Masters thesis]. South Dakota State University; 2016. [cited 2020 Aug 08]. Available from: http://openprairie.sdstate.edu/etd/1120.

Council of Science Editors:

Khan MRA. Data Center Load Forecast Using Dependent Mixture Model. [Masters Thesis]. South Dakota State University; 2016. Available from: http://openprairie.sdstate.edu/etd/1120

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