• 한국신재생에너지학회:학술대회논문집
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• 2009.06a
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• pp.554-556
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• 2009

Wind classification for exact estimation of wind energy resources was carried out using numerically simulated wind data for three years. The MM5(a fifth-generation Mesoscale Model), developed at Penn State University and the National Center for Atmospheric Research (NCAR), was used to estimate the wind fields in this study. We also use a variant of the K-mean clustering to classify the wind district and define the relation between districts. Wind estimated at surface and 100 m high at Busan area is classified into the 10 and 7 classes, respectively. These discrepancies of wind districts pattern at surface and upper air meteorological data indicates the quantity of wind resources can be changed according to the level of wind data used in estimation. Therefore, the estimation of wind district classification by reasonable wind data is utilized to build the effective policy for wind energy dissemination.

• Journal of Power Electronics
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• v.10 no.6
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• pp.769-776
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• 2010

The ultracapacitor module has recently been recast for use in hybrid energy storage systems (HESSs). As a result, accurate state-of-charge (SOC) estimation for an ultracapacitor module is as important as that of primary sources in order to be utilized efficiently in an energy storage system (ESS). However, while SOC estimation via the open-circuit voltage (OCV) method is generally used due to its linear characteristics compared with other ESSs, this method results in many errors in cases of highcurrent charging/discharging within a short time period. Accordingly, this paper introduces a dynamic SOC estimation algorithm that is capable of SOC compensation of an ultracapacitor module even when there is a current input and output. A cycle profile that simulates the operating conditions of a mild-HEV was applied to a vehicle simulator to verify the effectiveness of the proposed algorithm.

• The Transactions of the Korean Institute of Power Electronics
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• v.26 no.3
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• pp.183-191
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• 2021

A battery management system (BMS) provides some functions for ensuring safety and reliability that includes algorithms estimating battery states. Given the changes caused by various operating conditions, the state-of-health (SOH), which represents a figure of merit of the battery's ability to store and deliver energy, becomes challenging to estimate. Machine learning methods can be applied to perform accurate SOH estimation. In this study, we propose a Long-Term Recurrent Convolutional Network (LRCN) that combines the Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM) to extract aging characteristics and learn temporal mechanisms. The dataset collected by the battery aging experiments of NASA PCoE is used to train models. The input dataset used part of the charging profile. The accuracy of the proposed model is compared with the CNN and LSTM models using the k-fold cross-validation technique. The proposed model achieves a low RMSE of 2.21%, which shows higher accuracy than others in SOH estimation.

• Proceeding of Spring/Autumn Annual Conference of KHA
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• 2006.11a
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• pp.328-333
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• 2006

A few methodologies have been recently developed to estimate the environmental affect when various materials and components are used in building life cycle. The direct survey method has limitations to analyze the environmental problems because of the limit of survey scope and cost. Therefore, another indirect method has been developed as alternatives. The indirect method is represented as input-output analysis. This paper aimed at analyzing the estimation the environmental affect of building materials and works at building construction, utilizing the input-output analysis as a indirect estimation method. The results suggested that the building works is overally responsible for the energy consumption and $CO_2$ emission. In other words, Over the 80% of the total consumption and $CO_2$ emission are resulted at the building work.

• Proceedings of the KIEE Conference
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• 2000.07a
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• pp.242-244
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• 2000

This paper presents a method for real-time estimation of TCSC quantity in order to enhance the power system transient stability energy margin using fuzzy neural network in multi-machine system. This paper has two parts, the first part is to estimate the energy margin. To set critical energy, we use the potential energy boundary surface(PEBS) method which one of the transient energy function(TEF) method. And the second is to determine the TCSC quantify and the line to be injected. In order to make training data in this step, we use genetic algorithm. The proposed method is applied to 6-bus, 7-line, 4-machine model system to show its effectiveness.

• 한국태양에너지학회:학술대회논문집
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• 2008.04a
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• pp.142-147
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• 2008

Limited fossil fuels and unstable energy supply are considered as one of the critical problems in architecture requiring large amounts of energy. In order to this challenge, environment-friendly architecture design is required. Especially, Application of solar energy as a clean energy source and one kind of renewable energy. Many sites however are mainly concentrated in the developed countries, and are scarcer within the developing world. Simple radiation estimation models using meteorologically observed input parameters are often used in the applications requiring rough estimations of solar radiation. In this paper, measurements of global solar radiation and cloud data hours in climatological locations in South Korea are used to develop an estimation in solar radiation. The results of measured data are compared with the results obtained from equations, internationally accepted correlations.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2007.05a
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• pp.179-180
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• 2007

• Proceedings of the Korean Nuclear Society Conference
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• 1998.10a
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• pp.181-181
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• 1998

• Proceedings of the Korean Nuclear Society Conference
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• 1999.05a
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• pp.277-277
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• 1999

• Proceedings of the Korean Nuclear Society Conference
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• 2005.05a
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• pp.493-494
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• 2005