• Journal of Industrial Technology
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• v.30 no.A
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• pp.9-15
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• 2010

Recently, Smart Grid is a emerging topic in power and communication industry. Smart Grid refers to a evolution of the electricity supply infrastructure that monitors, protects, and intelligently optimize the operation of the interconnected elements including various type of generators, power grid, building/home automation system and end-use consumers. In order to successful implementation of Smart Grid, energy management function will be the key factor that coordinates and optimally controls the various loads according to the operating condition and environments, and the load patterns in residential and commercial building will be required as fundamental element for load management. In this study, we collects many types of energy usage data of electric appliances, analyze their load curves, and make the general load patterns for electrical appliance.

• Proceedings of the KIEE Conference
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• 2007.11c
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• pp.97-100
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• 2007

There are regulations on each building for its classification and It is corresponding determined contract demand. For transformer's capability calculation algorithm, cumulated power information of each customer is used to analysis the correlation between power usage and Demand Rate. By modeling this using Least Square Method, it can be targeted to recognize the pattern of transformer use in the past and make a prediction on it in the future.

• Journal of the Korean Solar Energy Society
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• v.26 no.2
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• pp.69-77
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• 2006

Photovoltaic is an attractive technology to remote power applications, because of its reliability, low maintenance, and zero fuel requirements. In this paper represents residential PV system based on BESS(battery energy storage system) for managing the electric power, a pattern of daily operation considering the load characteristics of the house, the generation characteristics of PV power, and utility power leveling. For apply to control algorithm, we consider the load on monthly power consumption trend and daily usage pattern. As for the control of the proposed system, to increase the conversion efficiency of the PV power, bidirectional converter is used for MPPT and SPWM inverter. An experimental system is implemented, and some experimental results are provided to demonstrate the effectiveness of the proposed system.

• Proceedings of the KSR Conference
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• 2007.11a
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• pp.1511-1514
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• 2007

KORAIL is one of the biggest consumer in electric power market so it is necessary to investigate power consumption pattern and optimize power rates. This paper presents simulation program for electric power rates define program based on billing system database. Base on the substation annual power usage DB data, the characteristic of the substation power consumption are investigated and effective electrical billing system are compared each other. Through this program it is verified that we can save more then several hundred million won for a year. Also electric train air condition power consumption was measured to figure the method how to reduce peak energy consumption.

• Proceedings of the Korea Contents Association Conference
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• 2007.11a
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• pp.226-230
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• 2007

This paper proposes a local scheduling method based on user pattern for Volunteer computing project, Korea@Home. It enables Korea@Home participants to run the agent without disturbance. It is devised to prevent user's application from delay while running the agent and decreases the frequency of switching resource between the user and the agent. We analyze the user's patterns of donating computing resource with Korea@Home which is a representative volunteer computing project in Korea. It has contributed the computing power to several applications including climate prediction and virtual screening. It promotes the volunteers to participate continuously without disturbance and increases the potential computing power with non-disturbance scheduling based on user usage pattern for Volunteer Computing.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.4 no.4
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• pp.300-306
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• 2011

In this paper, we suggest a usage profiles for electric home appliances. In Korea, it is published the records for total consumption of electricity in a house but the electric home appliance consumption records in a households are not. To build the data, we must collect the usage of every appliances in a house and the information of the household which live in the house. Unfortunately, it is hard to get the data because of the worry about the breach of privacy. In this paper, we make a scenarios on the electricity consumption pattern of a few households type. Based on the conjecture, we make the power consumption profiles for some home appliances. Comparison to the total electric consumption records for a house, we found our scenarios are quite reasonable.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.97-115
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• 2022

Smart power grid is a user friendly system that transforms the traditional electric grid to the one that operates in a co-operative and reliable manner. Demand Response (DR) is one of the important components of the smart grid. The DR programs enable the end user participation by which they can communicate with the electricity service provider and shape their daily energy consumption patterns and reduce their consumption costs. The increasing demands of electricity owing to growing population stresses the need for optimal usage of electricity and also to look out alternative and cheap renewable sources of electricity. The solar and wind energy are the promising sources of alternative energy at present because of renewable nature and low cost implementation. The proposed work models a smart home with renewable energy units. The random nature of the renewable sources like wind and solar energy brings an uncertainty to the model developed. A stochastic dual descent optimization method is used to bring optimality to the developed model. The proposed work is validated using the simulation results. From the results it is concluded that proposed work brings a balanced usage of the grid power and the renewable energy units. The work also optimizes the daily consumption pattern thereby reducing the consumption cost for the end users of electricity.

• The Journal of the Korea Contents Association
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• v.22 no.8
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• pp.640-647
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• 2022

The purpose of this study was to investigate the factors influencing sleep quality of nursing students, focusing on depression, smartphone usage before sleep, and academic score. Among nursing students in the 3rd and 4th grades of two universities, 196 subjects were randomly selected. Data were collected from April to July 2021 using tool of sleep quality(PSQI-K), depression(Korean BDI-II), and smartphone usage before sleep, and the effect on sleep quality was analyzed through Stepwise Multiple Regression Analysis. As a result of the study, two factors, depression and smartphone usage before sleep, had 39.0% explanatory power for the sleep quality of nursing students, and were statistically significant. That is, the higher the depression level and the more frequent smartphone usage before sleep, the worse the sleep quality level. However, academic score was not a significant variable. In conclusion, in order to improve the sleep quality of nursing students, it is necessary to reduce the pattern of sleep disorders related to depression and to correct the lifestyle by reducing smartphone usage before sleep.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.8
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• pp.1792-1798
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• 2010

Home Gateway is always on for continuous services of home networks. Ubiquitous home networks are extended. so power consumption of home gateways increases by geometric progression. Our algorithm is for home gateway to sleep, listen or wakeup according to network traffic adaptively, as well as to keep always-on service. To do this, it traces the accumulated average of previous sleep periods. In addition to this basic algorithm, we make the profiles for user's living pattern per the day to reflect network usages in detail and adaptively. As the simulation results by comparing with overall accumulated average and per-day accumulated average, in case of the overall accumulated average, the difference the estimation and real value is distributed from 0.43% to 4%. In contrast of this, in case of the per-day accumulated average is distributed from 0.06% to 2%. From this results, we know the profiling of per-day usage pattern can help reduce the difference of the real sleep period and the estimated sleep period.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.4
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• pp.587-594
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• 2018

The purpose of this study is to propose a power demand volatility evaluation system based on LSTM and not to verify the accuracy of the demand module which is a core module, but to recognize the sudden change of power pattern by using deeplearning in the actual power demand monitoring system. Then we confirm the availability of the module. Also, we tried to provide a visualized report so that the manager can determine the fluctuation of the power usage patten by applying it as a module to the web based system. It is confirmed that the power consumption data shows a certain pattern in the case of government offices and hospitals as a result of implementation of the volatility evaluation system. On the other hand, in areas with relatively low power consumption, such as residential facilities, it was not appropriate to evaluate the volatility.