• International Journal of Fuzzy Logic and Intelligent Systems
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• v.14 no.4
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• pp.305-312
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• 2014

This paper discusses real-time peak shaving algorithms for a large-scale battery energy storage system (BESS). Although several transmission and distribution functions could be implemented for diverse purposes in BESS applications, this paper focuses on a real-time peak shaving algorithm for an energy time shift, considering wind power generation. In a high wind penetration environment, the effective load levels obtained by subtracting the wind generation from the load time series at each long-term cycle time unit are needed for efficient peak shaving. However, errors can exist in the forecast load and wind generation levels, and the real-time peak shaving operation might require a method for wind generation that includes comparatively large forecasting errors. To effectively deal with the errors of wind generation forecasting, this paper proposes a real-time peak shaving algorithm for threshold value-based peak shaving that considers fuzzy wind power generation.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.3
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• pp.183-191
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• 2013

We address the power generation mix problem that considers not only nuclear and fossil fuels such as oil, coal and LNG but also renewable energy technologies. Unlike nuclear or other generation technologies, the expansion plan of renewable energy is highly uncertain because of its dependency on the government policy and uncertainty associated with technology improvements. To address this issue, we conduct a delphi survey and forecast the capacity of renewable energy. We further propose a stochastic mixed integer programming model that determines an optimal capacity expansion and the amount of power generation using each generation technology. Using the proposed model, we test eight generation mix scenarios and particularly evaluate how much the expansion of renewable energy contributes to the total costs for power generation in Korea. The evaluation results show that the use of renewable energy incurs additional costs.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2019.08a
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• pp.283-284
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• 2019

• Communications for Statistical Applications and Methods
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• v.31 no.1
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• pp.105-127
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• 2024

We propose recurrent neural network (RNN) methods for forecasting realized volatility (RV). The data are RVs of ten major stock price indices, four from the US, and six from the EU. Forecasts are made for relative ratio of adjacent RVs instead of the RV itself in order to avoid the out-of-scale issue. Forecasts of RV ratios distribution are first constructed from which those of RVs are computed which are shown to be better than forecasts constructed directly from RV. The apparent asymmetry of RV ratio is addressed by the Piecewise Min-max (PM) normalization. The serial dependence of the ratio data renders us to consider two architectures, long short-term memory (LSTM) and gated recurrent unit (GRU). The hyperparameters of LSTM and GRU are tuned by the nested cross validation. The RNN forecast with the PM normalization and ratio transformation is shown to outperform other forecasts by other RNN models and by benchmarking models of the AR model, the support vector machine (SVM), the deep neural network (DNN), and the convolutional neural network (CNN).

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.30-45
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• 2024

This study introduces an effective method for predicting individual local tax delinquencies using prevalent machine learning and deep learning algorithms. The evaluation of credit risk holds great significance in the financial realm, impacting both companies and individuals. While credit risk prediction has been explored using statistical and machine learning techniques, their application to tax arrears prediction remains underexplored. We forecast individual local tax defaults in Republic of Korea using machine and deep learning algorithms, including convolutional neural networks (CNN), long short-term memory (LSTM), and sequence-to-sequence (seq2seq). Our model incorporates diverse credit and public information like loan history, delinquency records, credit card usage, and public taxation data, offering richer insights than prior studies. The results highlight the superior predictive accuracy of the CNN model. Anticipating local tax arrears more effectively could lead to efficient allocation of administrative resources. By leveraging advanced machine learning, this research offers a promising avenue for refining tax collection strategies and resource management.

• Journal of Korea Water Resources Association
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• v.49 no.5
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• pp.361-372
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• 2016

Reliable long-term dam inflow prediction is necessary for efficient multi-purpose dam operation in changing climate. Since 2000s the teleconnection between global climate indices (e.g., ENSO) and local hydroclimate regimes have been widely recognized throughout the world. To date many hydrologists focus on predicting future hydrologic conditions using lag teleconnection between streamflow and climate indices. This study investigated the utility of teleconneciton for predicting dam inflow with 1-month lead time at Andong dam basin. To this end 40 global climate indices from NOAA were employed to identify potential predictors of dam inflow, areal averaged precipitation, temperature of Andong dam basin. This study compared three different approaches; 1) dam inflow prediction using SWAT model based on teleconneciton-based precipitation and temperature forecast (SWAT-Forecasted), 2) dam inflow prediction using teleconneciton between dam inflow and climate indices (CIR-Forecasted), and 3) dam inflow prediction based on the rank of current observation in the historical dam inflow (Rank-Observed). Our results demonstrated that CIR-Forecasted showed better predictability than the other approaches, except in December. This is because uncertainties attributed to temporal downscaling from monthly to daily for precipitation and temperature forecasts and hydrologic modeling using SWAT can be ignored from dam inflow forecast through CIR-Forecasted approach. This study indicates that 1-month lead dam inflow forecast based on teleconneciton could provide useful information on Andong dam operation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.6B
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• pp.597-603
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• 2006

The purpose of this study is to improve the short term rainfall forecast skill using neural network model that can deal with the non-linear behavior between satellite data and ground observation, and minimize the flood damage. To overcome the geographical limitation of Korean peninsula and get the long forecast lead time of 3 to 6 hour, the developed rainfall forecast model took satellite imageries and wide range AWS data. The architecture of neural network model is a multi-layer neural network which consists of one input layer, one hidden layer, and one output layer. Neural network is trained using a momentum back propagation algorithm. Flood was estimated using rainfall forecasts. We developed a dynamic flood inundation model which is associated with 1-dimensional flood routing model. Therefore the model can forecast flood aspect in a protected lowland by levee failure of river. In the case of multiple levee breaks at main stream and tributaries, the developed flood inundation model can estimate flood level in a river and inundation level and area in a protected lowland simultaneously.

• Journal of Korean Society for Atmospheric Environment
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• v.13 no.1
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• pp.19-29
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• 1997

It is more likely that the degree of air quality degradation that we have faced would be much lessen, if integrated management of air pollution control and assessment had been fully enforced by the local administrations, not by the centralized environmental agency in the first place. As the selfgoverning local administrations have been established since 1995, the need for air quality control by the local administration or local agency is getting the ground. However, in practice, air quality control by the local administration rarely put into effect due to lack of basic data which cover the present trends of air quality in each local city or county and are necessary for decision making. The emissions of SO$_2$ in each city and county of Korea in 1993 were calculated in this study, based on energy consumptions and emission factors. The ambient concentrations of SO$_2$ also were estimated by applying modified Miller-Holtzworth model. Observed and estimated concentrations of SO$_2$ showed that about 17.5 percents of cities and counties in the country were more polluted than the target value, 20ppb/year. The emissions and ambient concentrations of SO$_2$ in each city and county in 2000, 2005, and 2010 were also forecasted, assumed business as usual senario. It was shown that, in 2010, the emissions of SO$_2$ will be 2.8 times more than those of 1993 and much of them are from industrial sector. Also shown that 38.3 percents of cities and counties will be more polluted than the target value and most of them are polluted areas in 1993. The methods and results of this study could be used in developing the efficient reduction strategies in each city and county.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.1-10
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• 2015

This paper offers some long term perspective on what has been happening to some baseball statistics for Korean professional baseball. The data used are league summaries by year over the period 1982-2013. For the baseball statistics, statistically significant positive correlations (p < 0.01) were found for doubles (2B), runs batted in (RBI), bases on balls (BB), strike outs (SO), grounded into double play (GIDP), hit by pitch (HBP), on base percentage (OBP), OPS, earned run average (ERA), wild pitches (WP) and walks plus hits divided by innings pitched (WHIP) increased with year. There was a statistically significant decreasing trend in the correlations for triples (3B), caught stealing (CS), errors (E), completed games (CG), shutouts (SHO) and balks (BK) with year (trend p < 0.01). The ARIMA model of Box-Jenkins is applied to find a model to forecast future baseball measures. Univariate time series results suggest that simple lag-1 models fit some baseball measures quite well. In conclusion, the single most important change in Korean professional baseball is the overall incidence of completed games (CG) downward. Also the decrease of strike outs (SO) is very remarkable.

• Journal of Internet Computing and Services
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• v.21 no.5
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• pp.119-127
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• 2020

As the electric vehicle (EV) market in South Korea grows, it is required to expand charging facilities to respond to rapidly increasing EV charging demand. In order to conduct a comprehensive facility planning, it is necessary to forecast future demand for electricity and systematically analyze the impact on the load capacity of facilities based on this. In this paper, we design and develop a Long Short-Term Memory (LSTM) neural network model that predicts the daily peak electric load at each charging station using the EV charging data of KEPCO. First, we obtain refined data through data preprocessing and outlier removal. Next, our model is trained by extracting daily features per charging station and constructing a training set. Finally, our model is verified through performance analysis using a test set for each charging station type, and the limitations of our model are discussed.