• KIPS Transactions on Computer and Communication Systems
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• v.8 no.4
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• pp.87-92
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• 2019

This paper describes the pattern analysis for data of the factory energy consumption by using machine learning method. While usual statistical methods or approaches require specific equations to represent the physical characteristics of the plant, machine learning based approach uses historical data and calculate the result effectively. Although rule-based approach calculates energy usage with the physical equations, it is hard to identify the exact equations that represent the factory's characteristics and hidden variables affecting the results. Whereas the machine learning approach is relatively useful to find the relations quickly between the data. The factory has several components directly affecting to the electricity consumption which are machines, light, computers and indoor systems like HVAC (heating, ventilation and air conditioning). The energy loads from those components are generated in real-time and these data can be shown in time-series. The various sensors were installed in the factory to construct the database by collecting the energy usage data from the components. After preliminary statistical analysis for data mining, time-series clustering techniques are applied to extract the energy load pattern. This research can attributes to develop Factory Energy Management System (FEMS).

• Journal of the Korea Society of Computer and Information
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• v.19 no.6
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• pp.49-59
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• 2014

Recently, wireless sensor networks(WSNs) are widely used for intrusion detection and ecology, environment, atmosphere, industry, traffic, fire monitoring. In this paper, an energy efficient clustering algorithm is proposed. The proposed algorithm forms clusters uniformly by selecting cluster head that optimally located based on receiving power. Besides, proposed algorithm can induce uniform energy consumption regardless of location of nodes by multi-hop transmission and MST formation with limited maximum depth. Through the above, proposed algorithm elongates network life time, reduces energy consumption of nodes and induces fair energy consumption compared to conventional LEACH and HEED. The results of simulation show that the proposed clustering algorithm elongates network life time through fair energy consumption.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.1
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• pp.25-32
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• 2014

In this paper, we proposed a partial-way set associative cache memory with an effective memory access time and low energy consumption. In the proposed set-associative cache memory, it is allowed to access only a 2-ways among 4-way at a time. Choosing ways to be accessed is made dynamically via the least significant two bits of the tag. The chosen 2 ways are sequentially accessed by the way selection bits that indicate the most recently referred way. Therefore, each entry in the way has an additional bit, that is, the way selection bit. In addition, instead of the 4-way LRU or FIFO algorithm, we can utilize a simple 2-way replacement policy. Simulation results show that the energy*delay product can be reduced by about 78%, 14%, 39%, and 15% compared with a 4-way set associative cache, a sequential-way cache, a way-tracking cache, and a way cache respectively.

• Journal of the Korea Society of Computer and Information
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• v.16 no.6
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• pp.21-30
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• 2011

In the global IT market, a lot of issues for responding to various environmental regulations emerged. In case of the data centers, it is consuming huge amounts of energy to maintain. So there have been various technical attempts as Consolidation, Virtualization, Optimization to efficiently manage energy and data storage to fix the problems. In this paper, we propose a new RAID(Redundant Array of Independent Disks) mechanism which is differing the intensity of power consumption and works to provide data protection and disaster recovery(backup, mirroring etc.) to stratify multiple volumes. G-RAID minimize the power consumption and the lower of I/O performance by selecting the volume depending on the frequency of data access while classifying the power consumption between volumes in storage system. Also, it is possible that a filesystem and block map information of G-RAID is processed by basic unit which is group located in a row for the blocks to work efficiently and can minimize the performance degradation of block mapping load by the access frequency in each groups. As a result, we obtained to elevate a little bit of response time caused by block relocation work, but showed the decrease of power consumption by 38%.

• Environmental Engineering Research
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• v.22 no.2
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• pp.175-185
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• 2017

As the energy and environmental problems are increasingly severe, researches about carbon dioxide emissions has aroused widespread concern. The accurate prediction of carbon dioxide emissions is essential for carbon emissions controlling. In this paper, we analyze the relationship between carbon dioxide emissions and influencing factors in a comprehensive way through correlation analysis and regression analysis, achieving the effective screening of key factors from 16 preliminary selected factors including GDP, total population, total energy consumption, power generation, steel production coal consumption, private owned automobile quantity, etc. Then fruit fly algorithm is used to optimize the parameters of least squares support vector machine. And the optimized model is used for prediction, overcoming the blindness of parameter selection in least squares support vector machine and maximizing the training speed and global searching ability accordingly. The results show that the prediction accuracy of carbon dioxide emissions is improved effectively. Besides, we conclude economic and environmental policy implications on the basis of analysis and calculation.

• KIEAE Journal
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• v.14 no.2
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• pp.37-46
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• 2014

University buildings are energy-guzzling facility that consume more than 10,000TOE within a campus annually. Even the consumption is on an upswing trend. Behind such high consumption are there cheap power rates for education facility, lack of high-efficiency equipment and ever-increasing use of various information equipment. Being keenly aware that greenhouse gas emission increases due to such rise of energy consumption, the present study carried out a case study. In the case study, the study chose the buildings of E university from top 10 universities that consume energy most in Seoul and examined the current status of their energy consumption and greenhouse gas emission. And then it set the reduction target of greenhouse gas by year. Putting aside a middle and long-termed strategy for later endeavor, it first established the 1st year's implementation plan (2014) for energy saving and greenhouse gas reduction with limited budget and according to greenhouse gas reduction target. The plan is specified as follows. Targets for energy saving are mainly divided into two sectors: machine equipment and electric equipment. 7 ideas were proposed. Three ideas to improve machine equipment are to replace with high-efficiency boilers and chillers and to adjust the position of the cooling tower. By doing so, it was estimated that energy could be saved by 176.34TOE in total and greenhouse gas could be reduced by 370.771t$CO_2$-eq. Four ideas to improve electric equipment include the replacement with LED lights, LED emergency lights and high-efficiency motors and the installation of motion sensors. It was calculated that such replacement could conserve 1,076.08TOE (electric energy) and reduce 2,181.420t$CO_2$-eq (greenhouse gas).

• Journal of Satellite, Information and Communications
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• v.10 no.3
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• pp.6-10
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• 2015

RF energy harvesting technology is a promising technology for generating the electrical power from ambient RF signal to operate low-power consumption devices(eg. sensor) in wireless communication networks. This paper, motivated by this and building upon existing CR(Cognitive Radio) network model, proposes a optimal control policy for RF energy harvesting CR networks model where secondary users that have low power consumption harvest ambient RF energy from transmission by nearby active primary users, while periodically sensing and opportunistically accessing the licensed spectrum to the primary user's network. We consider that primary users and secondary users are distributed as Poisson point processes and contact with their intended receivers at fixed distances. Finally we can derive the optimal frame duration, transmission power and density of secondary user from the proposed model that can maximize the secondary users's throughput under the given several conditions and suggest future directions of research.

• Journal of IKEEE
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• v.23 no.1
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• pp.134-142
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• 2019

As the development of internet of energy (IoE) technologies and spread of various electronic devices have diversified patterns of energy consumption, the reliability of demand prediction has decreased, causing problems in optimization of power generation and stabilization of power supply. In this study, we propose a deep learning method, 1-Dimention-Convolution and Bidirectional Long Short-Term Memory (1D-ConvBLSTM), that combines a convolution neural network (CNN) and a Bidirectional Long Short-Term Memory(BLSTM) for highly reliable demand forecasting by effectively extracting the energy consumption pattern. In experimental results, the demand is predicted with the proposed deep learning method for various number of learning iterations and feature maps, and it is verified that the test data is predicted with a small number of iterations.

• The KIPS Transactions:PartC
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• v.19C no.2
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• pp.135-144
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• 2012

All the servers in a traditional server cluster environment are kept On. If the request load reaches to the maximum, we exploit its maximum possible performance, otherwise, we exploit only some portion of maximum possible performance so that the efficiency of server power consumption becomes low. We can improve the efficiency of power consumption by controlling power mode of servers according to load situation, that is, by making On only minimum number of servers needed to handle current load while making Off the remaining servers. In the existing power mode control method, they used a static policy to decide server power mode at a fixed time interval so that it cannot adapt well to the dynamically changing load situation. In order to improve the existing method, we propose a dynamic server power control algorithm. In the proposed method, we keep the history of server power consumption and, based on it, predict whether power consumption increases in the near future. Based on this prediction, we dynamically change the time interval to decide server power mode. We performed experiments with a cluster of 30 PCs. Experimental results show that our proposed method keeps the same performance while reducing 29% of power consumption compared to the existing method. In addition, our proposed method allows to increase the average CPU utilization by 66%.

• KIPS Transactions on Computer and Communication Systems
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• v.4 no.6
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• pp.185-196
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• 2015

Energy aware server clusters aim to reduce power consumption at maximum while keeping QoS(Quality of Service) compared to energy non-aware server clusters. They adjust the power mode of each server in a fixed or variable time interval to let only the minimum number of servers needed to handle current user requests ON. Previous studies on energy aware server cluster put efforts to reduce power consumption further or to keep QoS, but they do not consider energy efficiency well. In this paper, we propose an energy efficient cluster management based on autonomous learning for energy aware server clusters. Using parameters optimized through autonomous learning, our method adjusts server power mode to achieve maximum performance with respect to power consumption. Our method repeats the following procedure for adjusting the power modes of servers. Firstly, according to the current load and traffic pattern, it classifies current workload pattern type in a predetermined way. Secondly, it searches learning table to check whether learning has been performed for the classified workload pattern type in the past. If yes, it uses the already-stored parameters. Otherwise, it performs learning for the classified workload pattern type to find the best parameters in terms of energy efficiency and stores the optimized parameters. Thirdly, it adjusts server power mode with the parameters. We implemented the proposed method and performed experiments with a cluster of 16 servers using three different kinds of load patterns. Experimental results show that the proposed method is better than the existing methods in terms of energy efficiency: the numbers of good response per unit power consumed in the proposed method are 99.8%, 107.5% and 141.8% of those in the existing static method, 102.0%, 107.0% and 106.8% of those in the existing prediction method for banking load pattern, real load pattern, and virtual load pattern, respectively.