Data Center Load Forecast Using Dependent Mixture Model

<p>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.</p>

Name thesis:degree_name

Master of Science (MS)

Level thesis:degree_level

Thesis - Open Access

Discipline thesis:degree_discipline

Electrical Engineering and Computer Science

Year dc:date.available

2016

Author dc:creator

• Khan, Md Riaz Ahmed

Contributors dc:contributor

• Reinaldo Tonkoski
• Semhar Michael

• data center
• load forcast
• mixture model
• Computer Engineering
• Data Storage Systems
• Electrical and Computer Engineering

Licence

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language

en

Repository record dc:identifier

https://openprairie.sdstate.edu/etd/1120

OAI identifier oai:identifier

oai:openprairie.sdstate.edu:etd-2120