• Proceedings of the SAREK Conference
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• 2009.06a
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• pp.940-945
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• 2009

Energy planning for hybrid energy system is important to increase the flexibility in the urban community and national energy systems. Expected maximum loads, load profiles and yearly energy demands are important input parameters to plan for the technical and environmental optimal energy system for a planning area. The method for energy demand prediction has been based on artificial neural networks(ANN). The advantage of ANN with respect to the other method is their ability of modeling a multivariable problem given by the complex relationships between the variables. This method can produce 10% of errors hourly load profile from individual building to urban community. As the results of this paper, energy demand prediction system has been developed based on simulink.

• Journal of the Korea Society of Computer and Information
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• v.22 no.7
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• pp.1-7
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• 2017

As the capacity of NAND flash module increases, the amount of RAM increases for caching and maintaining the FTL mapping information. In order to reduce the amount of mapping information managed in the RAM, a demand-based address mapping method stores the entire mapping information in the flash and some valid mapping information in the form of cache in the RAM so that the RAM can be used efficiently. However, when cache miss occurs, it is necessary to read the mapping information recorded in the flash, so overhead occurs to translate the address. If the RAM space is not enough, the cache hit ratio decreases, resulting in greater overhead. In this paper, we propose a method using two tables called TPMT(Translation Page Mapping Table) and SMT(Segmented Translation Page Mapping Table) to utilize both temporal locality and spatial locality more efficiently. A performance evaluation shows that this method can improve the cache hit ratio by up to 30% and reduces the extra translation operations by up to 72%, compared to the TPM scheme.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.418-419
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• 2006

After electricity power industry restructuring, "Long term power development plan", setting up by government, is replaced by "Basic plan of electricity supply and demand". In this basic plan, one of the most important factors is assessment of appropriate capacity margin. The benefit of GENCO is decided by the market price, and the price is largely affected by the level of reserve margin. As a consequence, appropriate reserve margin is determined by market power. However, Cost Based Pool(CBP) is a limited competitive market, and government policy for supply and demand is very important factor or reserve margin determination. This paper points out issues about existing reserve margin assessment method which is used in basic plan and suggests improved assessment method. In the case study, capacity margin is calculated by proposed assessment method and result shows the advantages of suggested method.

• 한국태양에너지학회:학술대회논문집
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• 2009.04a
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• pp.184-189
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• 2009

In order to improve the operation of energy systems, it is necessary for the urban communities to have reliable optimization routines, both computerized and manual, implemented in their organizations. However, before a production plan for the energy system units can be constructed, a prediction of the energy systems first needs to be determined. So, several methodologies have been proposed for energy demand prediction, but due to uncertainties in urban community, many of them will fail in practice. The main topic of this paper has been the development of a method for energy demand prediction at urban community. Energy demand prediction is important input parameters to plan for the energy planing. This paper presents a energy demand prediction method which estimates heat and electricity for various building categories. The method has been based on artificial neural networks(ANN). The advantage of ANN with respect to the other method is their ability of modeling a multivariable problem given by the complex relationships between the variables. Also, the ANN can extract the relationships among these variables by means of learning with training data. In this paper, the ANN have been applied in oder to correlate weather conditions, calendar data, schedules, etc. Space heating, cooling, hot water and HVAC electricity can be predicted using this method. This method can produce 10% of errors hourly load profile from individual building to urban community.

• Journal of the military operations research society of Korea
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• v.11 no.1
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• pp.1-7
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• 1985

A computer-aided simulation model for inventory control was developed using Apple II Plus micro-computer. The model forecasts quarterly demands with Single Exponential Smoothing method and simulates Supply Demand Review and Inventory Level Settings for each items. The simulation is based on the assumption that the demand occurrences have their own probability distributions.

• 한국태양에너지학회:학술대회논문집
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• 2008.04a
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• pp.304-310
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• 2008

Currently energy use planning council system is mandatory especially for the urban development project planned on a specified scale or more. The goal of existing demand prediction was to calculate the maximum load by multiplying energy load per unit area by building size. The result of this method may be exaggerated and has a limit in the information of period load. The paper suggests a new forecasting process based on standard unit household in order to upgrade the limit in demand prediction method of multi-family housing complex. The new process was verified by comparing actual using amount of multi-family housing complex to forecasting value of energy use plan.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.1
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• pp.110-117
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• 2015

Spare part management is very important to products that have large number of parts and long lifecycle such as automobile and aircraft. Supply chain must support immediate procurement for repair. However, it is not easy to handle spare parts efficiently due to huge stock keeping units. Qualified forecasting is the basis for the supply chain to achieve the goal. In this paper, we propose an agent based modeling approach that can deal with various factors simultaneously without mathematical modeling. Simulation results show that the proposed method is reasonable to describe demand generation process, and consequently, to forecast demand of spare parts in long-term perspective.

• ETRI Journal
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• v.41 no.3
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• pp.326-336
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• 2019

A wireless power transfer technique can solve the power capacity problem in wireless rechargeable sensor networks (WRSNs). The charging strategy is a wide-spread research problem. In this paper, we propose a demand-based charging strategy (DBCS) for WRSNs. We improved the charging programming in four ways: clustering method, selecting to-be-charged nodes, charging path, and charging schedule. First, we proposed a multipoint improved K-means (MIKmeans) clustering algorithm to balance the energy consumption, which can group nodes based on location, residual energy, and historical contribution. Second, the dynamic selection algorithm for charging nodes (DSACN) was proposed to select on-demand charging nodes. Third, we designed simulated annealing based on performance and efficiency (SABPE) to optimize the charging path for a mobile charging vehicle (MCV) and reduce the charging time. Last, we proposed the DBCS to enhance the efficiency of the MCV. Simulations reveal that the strategy can achieve better performance in terms of reducing the charging path, thus increasing communication effectiveness and residual energy utility.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.4
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• pp.534-541
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• 2012

This paper tries to elicit an optimal generation mix of Korea. Two approaches, using the screening curve method and taking advantage of a generation expansion planning tool, WASP-IV, are applied in getting the mix. The data used in this study is based on the 5th basic plan for long-term electricity supply and demand. The Load Duration Curve, that is needed for applying Screening Curve Method(SCM), is made based on the load profile in 2010. In our using SCM, the nuclear plant's operation characteristic, carbon emission cost and spinning reserve are considered. In using WASP-IV to get the adequate generation mix, the base and target demand forecasts in the 5th basic plan are used and the carbon emission cost is also considered. In this paper, It introduces the domestic adequacy generation mix in 2024 though SCM and WASP-IV.

• Earthquakes and Structures
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• v.3 no.3_4
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• pp.413-434
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• 2012

This paper presents a displacement-based seismic design method with damage control, in which the targets for the considered performance level are set as displacements and a damage distribution is proposed by the designer. The method is based on concepts of basic structural dynamics and of a reference single degree of freedom system associated to the fundamental mode with a bilinear behaviour. Based on the characteristics of this behaviour curve and on the requirements of modal spectral analysis, the stiffness and strength of the structural elements of the structure satisfying the target design displacement are calculated. The formulation of this method is presented together with the formulations of two other existing methods currently considered of practical interest. To illustrate the application of the proposed method, 5 reinforced concrete plane frames: 8, 17 and 25 storey regular, and 8 and 12 storey irregular in elevation. All frames are designed for a seismic demand defined by single earthquake record in order to compare the performances and damage distributions used as design targets with the corresponding results of the nonlinear step by step analyses of the designed structures subjected to the same seismic demand. The performances and damage distributions calculated with these analyses show a good agreement with those postulated as targets.