Temperature Distribution Prediction in Data Centers for Decreasing Power Consumption by Machine Learning

2015 ◽  
Author(s):  
Yuya Tarutani ◽  
Kazuyuki Hashimoto ◽  
Go Hasegawa ◽  
Yutaka Nakamura ◽  
Takumi Tamura ◽  
...  
Keyword(s):  
Machine Learning ◽  
Temperature Distribution ◽  
Power Consumption ◽  
Data Centers

scholarly journals Power consumption prediction in cloud data center using machine learning

2020 ◽  
Vol 10 (2) ◽  
pp. 1524 ◽  
Author(s):  
Deepika T. ◽  
Prakash P.
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Power Consumption ◽  
Data Center ◽  
Data Centers ◽  
Virtual Machines ◽  
Service Providers ◽  
Business World ◽  
Cloud Data ◽  
Cloud Data Centers

The flourishing development of the cloud computing paradigm provides several services in the industrial business world. Power consumption by cloud data centers is one of the crucial issues for service providers in the domain of cloud computing. Pursuant to the rapid technology enhancements in cloud environments and data centers augmentations, power utilization in data centers is expected to grow unabated. A diverse set of numerous connected devices, engaged with the ubiquitous cloud, results in unprecedented power utilization by the data centers, accompanied by increased carbon footprints. Nearly a million physical machines (PM) are running all over the data centers, along with (5 – 6) million virtual machines (VM). In the next five years, the power needs of this domain are expected to spiral up to 5% of global power production. The virtual machine power consumption reduction impacts the diminishing of the PM’s power, however further changing in power consumption of data center year by year, to aid the cloud vendors using prediction methods. The sudden fluctuation in power utilization will cause power outage in the cloud data centers. This paper aims to forecast the VM power consumption with the help of regressive predictive analysis, one of the Machine Learning (ML) techniques. The potency of this approach to make better predictions of future value, using Multi-layer Perceptron (MLP) regressor which provides 91% of accuracy during the prediction process.

Global and Local Clustering-Based Regression Models to Forecast Power Consumption in Buildings

2016 ◽  
pp. 207-234
Author(s):  
Gonzalo Vergara ◽  
Juan J. Carrasco ◽  
Jesus Martínez-Gómez ◽  
Manuel Domínguez ◽  
José A. Gámez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Power Consumption ◽  
Electric Power ◽  
Principal Component ◽  
Real Data ◽  
Machine Learning Techniques ◽  
Electric Power Consumption ◽  
Support Vector ◽  
Learning Machines ◽  
The University

The study of energy efficiency in buildings is an active field of research. Modeling and predicting energy related magnitudes leads to analyze electric power consumption and can achieve economical benefits. In this study, classical time series analysis and machine learning techniques, introducing clustering in some models, are applied to predict active power in buildings. The real data acquired corresponds to time, environmental and electrical data of 30 buildings belonging to the University of León (Spain). Firstly, we segmented buildings in terms of their energy consumption using principal component analysis. Afterwards, we applied state of the art machine learning methods and compare between them. Finally, we predicted daily electric power consumption profiles and compare them with actual data for different buildings. Our analysis shows that multilayer perceptrons have the lowest error followed by support vector regression and clustered extreme learning machines. We also analyze daily load profiles on weekdays and weekends for different buildings.

scholarly journals Outdoor Air-Cooling System for a Computer Room and Its Corresponding Energy-Saving Effect

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5719
Author(s):  
JiHyun Hwang ◽  
Taewon Lee
Keyword(s):  
Power Consumption ◽  
Energy Saving ◽  
Data Centers ◽  
Cooling System ◽  
Stable Operation ◽  
Air Cooling ◽  
Outdoor Air ◽  
Air Conditioners ◽  
Air Cooling System ◽  
Saving Effect

The recent expansion of the internet network and rapid advancements in information and communication technology are expected to lead to a significant increase in power consumption and the number of data centers. However, these data centers consume a considerable amount of electric power all year round, regardless of working days or holidays; thus, energy saving at these facilities has become essential. A disproportionate level of power consumption is concentrated in computer rooms because air conditioners in these rooms are required to operate throughout the year to maintain a constant indoor environment for stable operation of computer equipment with high-heat release densities. Considerable energy-saving potential is expected in such computer rooms, which consume high levels of energy, if an outdoor air-cooling system and air conditioners are installed. These systems can reduce the indoor space temperature by introducing a relatively low outdoor air temperature. Therefore, we studied the energy-saving effect of introducing an outdoor air-cooling system in a computer room with a disorganized arrangement of servers and an inadequate air conditioning system in a research complex in Korea. The findings of this study confirmed that annual energy savings of up to approximately 40% can be achieved.

Sign in / Sign up

Export Citation Format

Share Document