• Journal of Convergence for Information Technology
• /
• v.9 no.1
• /
• pp.19-27
• /
• 2019

In recent years, with the advancement of IT technology, we expect data size of the world to increase 10 times in 2025. The rapid development of this Internet technology leads to the downsizing of the server system of the data center which manages and operates the data, the capacity of the storage medium, and the power consumption of the data center. In this paper, we propose an energy conservation policy and analyze it in real time by analyzing the power consumption pattern of the server system of the data center. The proposed system can monitor and analyze the power consumption pattern of the individual server system in the data center, and it can be expected that about 10% of the total power consumption of the data center will be saved by efficiently managing the actual operation time of the server system.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.9 no.2
• /
• pp.75-80
• /
• 2014

Energy consumption of sensor nodes is minimized because it has limited energy generator in wireless sensor network. Electronic shelf label system is one of application fields using wireless sensor networks. Battery size of small apparatus for displaying price is restricted. Therefore its current consumption have to be minimized. Furthermore the method for minimization of peak current would be considered because life cycle of coin battery used to display or RF is vulnerable to intensity of drain current. In this paper, we analyze current consumption pattern of low-power electronic shelf label system. Then we propose the method for minimization of current consumption by modification of software and hardware. Current consumption of the system using proposed method are approximately 15 to 20 percent lower than existing system and the life cycle of the system is approximately 10 percent higher than existing system.

• The KIPS Transactions:PartD
• /
• v.16D no.3
• /
• pp.307-316
• /
• 2009

In this paper, the spatiotemporal data mining methodology for detecting a cycle of power consumption pattern with the change of time and spatial was proposed, and applied to the power consumption data collected by GIS-AMR system with an aim to use its resulting knowledge in real world applications. First, partial clustering method was applied for cluster analysis concerned with the aim of customer's power consumption. Second, the patterns of customer's power consumption data which contain time and spatial attribute were detected by 3D cube mining method. Third, using the calendar pattern mining method for detection of cyclic patterns in the various time domains, the meanings and relationships of time attribute which is previously detected patterns were analyzed and predicted. For the evaluation of the proposed spatiotemporal data mining, we analyzed and predicted the power consumption patterns included the cycle of time and spatial feature from total 266,426 data of 3,256 customers with high power consumption from Jan. 2007 to Apr. 2007 supported by the GIS-AMR system in KEPRI. As a result of applying the proposed analysis methodology, cyclic patterns of each representative profiles of a group is identified on time and location.

• Proceedings of the KIEE Conference
• /
• 2002.07a
• /
• pp.395-397
• /
• 2002

Recently, electric heating using midnight power has brought a rapid increase. Therefore, the increase of midnight power use has much influenced on midnight load pattern and share more than 20% of total midnight electricity consumption. Hence, the distortion of load by using midnight power apparatus is severe and made the usage pattern ineffective electricity usage pattern. This paper analyze the load pattern of midnight power apparatus and policies that can effectively build midnight load. In addition, it provides policies on shifting midnight power apparatus load.

• Proceedings of the KIEE Conference
• /
• 2005.11b
• /
• pp.151-155
• /
• 2005

An electricity consumption is closely related to the economic growth structure. The change of economic growth structure affects the pattern of electricity consumption widely and severely. This paper gives that the primary changing factors of electricity growth are economic growth, change of industry structure(the change of electricity consumption ratio in case of residential sector), and the effect of electricity saying. It gives a model to analyze the influence of GDP to the change of electricity consumption patterns by sector through the period of pre and post 1998(IMF, financial crisis) to observe the contribution of each factor to the growth of electricity demand. It is anticipated that this study shows the feasible scheme of economic structure to become the developed country.

• 한국태양에너지학회:학술대회논문집
• /
• 2008.04a
• /
• pp.336-341
• /
• 2008

For the last half a century, Korea has been experienced rapid economic growth and industrialization development, however they cause serious problems that environment pollution and energy shortage are appeared, and the biggest problem that we are now confronted are required solutions through all over the world. Now, Korea's energy consumption is the 10th in the whole world. Among them, energy for buildings, about 25% in the whole Energy which spend in Korea, is very serious. Especially, the energy consumption of school buildings which have heating & cooling system according to improvements of educational environment are rapidly increasing. These features are explicit in the University, Because it has lots of colleges and facilities for lecture, experiment, and research. Especially, electric power consumption account for 75 percent of energy consumption in educational institutions. Accordingly, it is important to understand and analyze the pattern of electricity energy consumption which is used. This study attempts to appoint the place which is one of university and to investigate the characteristics of energy consumption like electricity, gas, oil.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.58 no.1
• /
• pp.28-34
• /
• 2009

In this study firstly we survey the calculation method and the characteristics of the way of estimating CBL(Customer BaseLine Load) that is important calculation tool for DRP internationally. Also we analyze the power consumption pattern using the 15 minutes load profiles of about 120,000 customers in domestic. Based on this pattern, we provide the CBL calculation method that can be utilized in DRP to save the cost, and analyze the accuracy of the CBL calculation proposed in this paper through the simulation.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 1993.10a
• /
• pp.218-223
• /
• 1993

An UBET(Upper Bound Elemental Technique) program has been developed to analyze forging load, die-cavity filling and effective strain distribution for flashless forging. To analyze the process easily, it is suggested that the deformation is divided into two different parts. Those are axisymmetric part in corner and plane-strain part in lateral. The total power consumption is minimized through combination of two deformation parts by building block method, from which the upper-bound forging load, the flow pattern, the grid pattern, the veocity distribution and the effective strain are determined. To show the merit of flashless forging, the result of flashless and flash forging processes are compared through theory and experiment. Experiments have been carried out with plasticine billets at room temperature. The theoretical predictions of the forging load and the flow pattern are in good agreement with the experimental results.

• Journal of IKEEE
• /
• v.23 no.4
• /
• pp.1373-1380
• /
• 2019

In order to increase energy efficiency, developments of the advanced metering infrastructure (AMI) in the smart grid technology have recently been actively conducted. An essential part of AMI is analyzing power consumption and forecasting consumption patterns. In this paper, we analyze the power consumption and summarized the data errors. Monthly power consumption patterns are also analyzed using the k-means clustering algorithm. Forecasting the consumption pattern by each household is difficult. Therefore, we first classify the data into 100 clusters and then predict the average of the next day as the daily average of the clusters based on the deep neural network. Using practically collected AMI data, we analyzed the data errors and could successfully conducted power forecasting based on a clustering technique.

• ETRI Journal
• /
• v.38 no.5
• /
• pp.885-895
• /
• 2016

The lowest power mode (passive-standby mode) was proposed for reducing the power consumption of set-top boxes in a standby state when not receiving content. However, low-power set-top boxes equipped with the lowest power mode have been rarely commercialized because of their low-quality user experience. In the lowest power mode, they deactivates almost all of operational modules and processes, and thus require dozens of seconds for activation latency (that is, the latency for activating all modules of the set-top boxes in a standby state). They are not even updated in a standby state because they deactivate their network interfaces in a standby state. This paper proposes an adaptive standby mode scheduling method for improving the user experience of such boxes. Set-top boxes using the proposed method can analyze the activation pattern and find the frequently used time period (that is, when the set-top boxes are frequently activated). They prepare for their activation during this frequently used time period, thereby reducing the activation latency and enabling their update in a standby state.