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On the Energy Consumption Forecasting of Data Centers Based on Weather Conditions: Remote Sensing and Machine Learning Approach [article]

Georgios Smpokos, Mohamed A. Elshatshat, Athanasios Lioumpas, Ilias Iliopoulos
2018 arXiv   pre-print
The energy consumption of Data Centers (DCs) is a very important figure for the telecommunications operators, not only in terms of cost, but also in terms of operational reliability.  ...  A relation between the energy consumption and the weather conditions would indicate that weather forecast models could be used for predicting energy consumption of DCs.  ...  ACKNOWLEDGMENT Part of this work has been funded by the FIESTA-IoT project, with GA number: 643943. The REAL-DC testbed has been utilized for gathering the data that have been used in this work.  ... 
arXiv:1804.01754v2 fatcat:s63xemg5jreq5fuadgmrfanlju

A Method for Local Analysis of Electric Energy Monitoring Data

Haowen Wu, Chong Wang, Yudi Zhou, Wenwang Xie, Chen Chen
2022 International Journal of Innovative Computing, Information and Control  
To address this problem, a short-term energy prediction system based on edge computing architecture is proposed, in which data acquisition, data processing and regression prediction are distributed in  ...  In the field of energy management, energy prediction can be carried out by sensing and analyzing dynamic environmental information of the energy consumption side, which provides decision support for energy  ...  In [10] , an energy consumption prediction method based on support vector machine was proposed as the foundation for smart grid.  ... 
doi:10.24507/ijicic.18.01.105 fatcat:7nlz2jzrubdedgtivedlmwt4py

An Energy Prediction Model for Cloud Data Centers Through Performance Counter

Meng Sa, Sun Peng, Luo Jie, Xu Han
2019 International Journal of Performability Engineering  
Then, we purpose an energy prediction model to estimate the energy consumption of servers in cloud data centers based on performance counters of their processors.  ...  In this paper, we first discuss the energy consumption problems of cloud data centers and summarize the related work.  ...  Related Work The energy model for servers in cloud data centers has gained increasing attention.  ... 
doi:10.23940/ijpe.19.11.p15.29622971 fatcat:xptab5uyyje33aswe26pjpalou

Energy-Aware High Performance Computing: A Taxonomy Study

Chang Cai, Lizhe Wang, Samee U. Khan, Jie Tao
2011 2011 IEEE 17th International Conference on Parallel and Distributed Systems  
Based on a taxonomy of methods and system scales, this paper reviews the current status of energy-aware HPC research and summarizes open questions and research directions of software architecture for future  ...  This paper is devoted to categorize energy-aware computing methods for the high-end computing infrastructures, such as servers, clusters, data centers, and Grids/Clouds.  ...  Thermal-aware methods Some research develop thermal-aware methods to reduce energy-consumption of air-condition systems in data centers.  ... 
doi:10.1109/icpads.2011.59 dblp:conf/icpads/CaiWKT11 fatcat:2frg66nlyrf6djsbnmnfggs57u

Research on Energy Management Strategy of Captive Power Plant Based on Hybrid Method

Juan Liu, Xixia Huang, Xiaoli Liu, Zhiliang Han
2019 Sensors and materials  
Considering the irregularity and incompleteness of the data, a data-mining-based boiler energy management analysis method was proposed in this paper.  ...  This study adopted the random forest and recursive feature elimination (RF-RFE) method to select the features of distributed control system (DCS) monitoring data.  ...  The feature selection method based on information interaction was used to optimize the input features of the model.  ... 
doi:10.18494/sam.2019.2314 fatcat:p2dkaij2mvalfnrgxu5th4zfcm

Hybrid Deep Neural Network Model for Multi-Step Energy Prediction of Prosumers

Marcel Antal, Liana Toderean, Tudor Cioara, Ionut Anghel
2022 Applied Sciences  
The results show that, even on energy data streams, the model offers a good prediction accuracy for small- and medium-scale prosumers.  ...  In this paper, we propose a 24-steps-ahead energy prediction model that integrates clustering and multilayer perceptron classification models used to detect the classes of energy profiles and multilayer  ...  Gabriel Antonesi for helping with the experiments for evaluating the prediction model. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app12115346 fatcat:b5m66pqfmbff3egwnsux3mdovq

Regression Cloud Models and Their Applications in Energy Consumption of Data Center

Yanshuang Zhou, Na Li, Hong Li, Yongqiang Zhang
2015 Journal of Electrical and Computer Engineering  
As cloud data center consumes more and more energy, both researchers and engineers aim to minimize energy consumption while keeping its services available.  ...  In this paper, we analyzed linear and nonlinear regression energy model based on performance counters and system utilization and proposed a support vector regression energy model.  ...  Acknowledgment This work was supported in part by the science and technology planning project of Hebei province (no. 12210334).  ... 
doi:10.1155/2015/143071 fatcat:aunewt2h25fhvezqncnpmtsbde

Estimating Power/Energy Consumption in Database Servers

Manuel Rodriguez-Martinez, Harold Valdivia, Jaime Seguel, Melvin Greer
2011 Procedia Computer Science  
The explosive growth in the size of data centers, coupled with the wide-spread use of virtualization technology has brought power and energy consumption as major concerns for data center administrators  ...  Since database servers comprise one of the most popular and important server applications deployed in such facilities, it becomes necessary to have accurate cost models that can predict the power and energy  ...  Comparisons of measured energy consumption with the predictions made by Methods A and B.  ... 
doi:10.1016/j.procs.2011.08.022 fatcat:42holky2xjhjxdgqmqdoenorfy

IEEE Access Special Section Editorial: Convergence of Sensor Networks, Cloud Computing, and Big Data in Industrial Internet of Things

Lei Shu, Vincenzo Piuri, Chunsheng Zhu, Xuebin Chen, Mithun Mukherjee
2020 IEEE Access  
In the simulation experiment, the proposed algorithm can effectively restrict the link loss of the data center, which reduces the energy cost in the data center.  ...  In the article ''An industrial Internet of Things feature selection method based on potential entropy evaluation criteria,'' by Zhao and Dong, the authors present a novel feature selection approach based  ...  He has published over 400 papers in related conferences, journals, and books in the areas of sensor networks and the Internet of Things.  ... 
doi:10.1109/access.2020.3037614 fatcat:h27mydy5anggroyhsf5wipre6m

Optimization of Printing and Dyeing Energy Consumption Based on Multimedia Machine Learning Algorithm

Xulan Zhang, Yue Yu, Chin-Ling Chen
2022 Security and Communication Networks  
Based on the application of data warehouse and combined with historical data, a new energy consumption optimization evaluation method is proposed.  ...  On this basis, a low-power scheduling strategy for typical data center applications is designed and implemented.  ...  Based on the data of printing and dyeing process, combined with order related information, process parameter related information, and energy-related multimedia information, the energy consumption category  ... 
doi:10.1155/2022/1960425 fatcat:5yeu665zt5d63l2xkizpqlnewu

A Novel CNC Milling Energy Consumption Prediction Method Based on Program Parsing and Parallel Neural Network

Jianhua Cao, Xuhui Xia, Lei Wang, Zelin Zhang, Xiang Liu
2021 Sustainability  
Aiming at the drawbacks of existing CNC milling energy consumption prediction methods in terms of efficiency and precision, a novel milling energy consumption prediction method based on program parsing  ...  Moreover, based on the improved parallel neural network, the mapping relationship between the energy consumption parameters of each CNC instruction and the milling energy consumption is constructed.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/su132413918 fatcat:6u4ox4djhjh4biwu6bo7eitpwe

Extracting Influential Factors for Building Energy Consumption via Data Mining Approaches

Jihoon Jang, Jinmog Han, Min-Hwi Kim, Deuk-won Kim, Seung-Bok Leigh
2021 Energies  
This study analyzed outdoor and indoor data collected from buildings to find out the conditions of rooms that had a significant effect on heating and cooling energy consumption.  ...  To examine the conditions of the rooms in each building, the energy consumption importance priority was derived using the Gini importance of the random forest algorithm on external and internal environmental  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/en14248505 fatcat:gkfo246q6vcwdglbfc4vbqlkfu

Energy Consumption Modeling and Prediction in the Cloud Data Centers

Sara Diouani, Research Foundation for Development and Innovation in Science and Engineering, Casablanca, Morocco, Hicham Medromi
2020 Journal of Engineering Science and Technology Review  
Various methods aimed to enhance the energy efficiency in data centers have been developed in the field related to the development of resource allocation.  ...  Energy efficiency has turned into an undeniably essential worry in data centers as a result of issues related to energy consumption, including capital costs, working costs, and natural effect.  ...  This is an Open Access article distributed under the terms of the Creative Commons Attribution License ______________________________  ... 
doi:10.25103/jestr.133.25 fatcat:or6u4yy4hjhqzpalruq43zqjgi

Machine Learning (ML)-Centric Resource Management in Cloud Computing: A Review and Future Directions [article]

Tahseen Khan, Wenhong Tian, Rajkumar Buyya
2021 arXiv   pre-print
Finally, we propose potential future research directions based on identified challenges and limitations in current research.  ...  In cloud computing, Infrastructure as a Service (IaaS) is one of the most important and rapidly growing fields.  ...  usage prediction and multi-step prediction for limiting the unnecessary VM migrations to avoid overheads and wasted energy consumption in data centers.  ... 
arXiv:2105.05079v1 fatcat:nvlsincidjejnfpddlg7mswybm

A Systematic Review of Energy Management Strategies for Resource Allocation in the Cloud: Clustering, Optimization and Machine Learning

Stanly Jayaprakash, Manikanda Devarajan Nagarajan, Rocío Pérez de Prado, Sugumaran Subramanian, Parameshachari Bidare Divakarachari
2021 Energies  
A key aspect for cloud data centers is to achieve management methods to reduce energy consumption, increasing the profit and reducing the environmental impact, which is critical in the deployment of leading-edge  ...  Moreover, we analyze how machine learning methods such as deep neural network (DNN), random forest, and support vector machine (SVM) are applied to the prediction of energy consumption in the cloud, showing  ...  Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/en14175322 fatcat:ivxj4gyobbdvlafyzbjcace76y
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