• Magazine of the Korean Society of Agricultural Engineers
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• v.37 no.3_4
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• pp.48-60
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• 1995

A flood - flow forecasting system model of river basins has been developed in this study. The system model consists of the data management system(the observation and telemetering system, the rainfall forecasting and data-bank system), the flood runoff simulation system, the reservoir operation simulation system, the flood forecasting simulation system, the flood warning system and the user's menu system. The Multivariate Rainfall Forecasting model, Meteorological factor regression model and Zone expected rainfall model for rainfall forecasting and the Streamflow synthesis and reservoir regulation(SSARR) model for flood runoff simulation have been adopted for the development of a new system model for flood - flow forecasting. These models are calibrated to determine the optimal parameters on the basis of observed rainfall, 7 streamfiow and other hydrological data during the past flood periods.

• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.2193-2196
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• 2008

In this study, the new methodology such as support vector machines neural networks model (SVM-NNM) using the statistical learning theory is introduced to forecast flood stage in Nakdong river, Republic of Korea. The SVM-NNM in hydrologic time series forecasting is relatively new, and it is more problematic in comparison with classification. And, the multilayer perceptron neural networks model (MLP-NNM) is introduced as the reference neural networks model to compare the performance of SVM-NNM. And, for the performances of the neural networks models, they are composed of training, cross validation, and testing data, respectively. From this research, we evaluate the impact of the SVM-NNM and the MLP-NNM for the forecasting of the hydrologic time series in Nakdong river. Furthermore, we can suggest the new methodology to forecast the flood stage and construct the optimal forecasting system in Nakdong river, Republic of Korea.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.2
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• pp.149-154
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• 2013

Short term load forecasting for electric power demand is essential for stable power system operation and efficient power market operation. High accuracy of the short term load forecasting can keep the power system more stable and save the power market operation cost. We propose an optimal coefficient selection method for exponential smoothing model in short term load forecasting on weekdays. In order to find the optimal coefficient of exponential smoothing model, load forecasting errors are minimized for actual electric load demand data of last three years. The proposed method are verified by case studies for last three years from 2009 to 2011. The results of case studies show that the average percentage errors of the proposed load forecasting method are improved comparing with errors of the previous methods.

• Clean Technology
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• v.28 no.2
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• pp.138-146
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• 2022

Electricity has become a factor that dramatically affects the market economy. The day-ahead system marginal price determines electricity prices, and system marginal price forecasting is critical in maintaining energy management systems. There have been several studies using mathematics and machine learning models to forecast the system marginal price, but few studies have been conducted to develop, compare, and analyze various machine learning and deep learning models based on a data-driven framework. Therefore, in this study, different machine learning algorithms (i.e., autoregressive-based models such as the autoregressive integrated moving average model) and deep learning networks (i.e., recurrent neural network-based models such as the long short-term memory and gated recurrent unit model) are considered and integrated evaluation metrics including a forecasting test and information criteria are proposed to discern the optimal forecasting model. A case study of South Korea using long-term time-series system marginal price data from 2016 to 2021 was applied to the developed framework. The results of the study indicate that the autoregressive integrated moving average model (R-squared score: 0.97) and the gated recurrent unit model (R-squared score: 0.94) are appropriate for system marginal price forecasting. This study is expected to contribute significantly to energy management systems and the suggested framework can be explicitly applied for renewable energy networks.

• Water for future
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• v.26 no.4
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• pp.117-125
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• 1993

A flood -flow management system model of river basin has been developed in this study. The system model consists of the observation and telemetering system, the rainfall forecasting and data-bank system, the flood runoff simulation system, the dam operation simulation system, the flood forecasting simulation system and the flood warning system. The Multivariate model(MV) and Meterological-factor regression model(FR) for rainfall forecasting and the Streamflow synthesis and reservoir regulation(SSARR) model for flood runoff simulation have been adopted for the development of a new system model for flood-flow management. These models are calibrated to determine the optimal parameters on the basis of observed rainfall, streamflow and other hydrological data during the past flood periods. The flood-flow management system model with SSARR model(FFMM-SR,FFMM-SR(FR) and FFMM-SR(MV)), in which the integrated operation of dams and rainfall forecasting in the basin are considered, is then suggested and applied for flood-flow management and forecasting. The results of the simulations done at the base stations are analysed and were found to be more accurate and effective in the FFMM-SR and FFMM0-SR(MV).

• Korean Management Science Review
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• v.27 no.2
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• pp.31-44
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• 2010

It is very important to forecast demand and determine the optimal procurement quantities of spare parts. The Army has been forecasting demand not with actual usage of spare parts but with request quantities. However, the Army could not purchase all of forecasted demand quantities due to budget limit. Thus, the procurement quantities depend on the item managers' intuition and their meetings. The system currently used contains many problems. This study suggests a new determination procedure; 1) forecasting demand method based on actual usage, 2) determining procurement method through LP model with budge and other constraints. The newly determined quantities of spare parts is verified in the simulation model, that represents the real operational and maintenance situation to measure the operational availability. The result shows that the new forecasting method with actual usage improves the operational availability. Also, the procurement determination with LP improves the operational availability as well.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.3
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• pp.493-499
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• 2017

This paper proposes a combined very-short-term and short-term multi-variate time-series model for 24 hour load forecasting. First, the best model for very-short-term and short-term load forecasting is selected by considering the least error value, and then they are combined by the optimal forecasting time. The actual load data of industry complex is used to show the effectiveness of the proposed model. As a result the load forecasting accuracy of the combined model has increased more than a single model for 24 hour load forecasting.

• IE interfaces
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• v.16 no.4
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• pp.400-410
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• 2003

Most of the demand forecasting which have been studied is about long-term time series over 15 years demand forecasting. In this paper, we set up the most optimal ARIMA model for the short-term time series demand forecasting and suggest demand forecasting system for short-term time series by appraising suitability and predictability. We are going to use the univariate ARIMA model in parallel with the bivariate transfer function model to improve the accuracy of forecasting. We also analyze the effect of advertisement cost, scale of branch stores, and number of clerk on the establishment of marketing policy by applying statistical methods. After then we are going to show you customer's needs, which are number of buying products. We have applied this method to forecast the annual sales of refrigerator in four branch stores of A company.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1419-1424
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• 2018

Very short-term electric load forecasting is essential for real-time power system operation. In this paper, a very short-term electric load forecasting technique applying the Kalman filter algorithm is proposed. In order to apply the Kalman filter algorithm to electric load forecasting, an electrical load forecasting algorithm is defined as an observation model and a state space model in a time domain. In addition, in order to precisely reflect the noise characteristics of the Kalman filter algorithm, the optimal error covariance matrixes Q and R are selected from several experiments. The proposed algorithm is expected to contribute to stable real-time power system operation by providing a precise electric load forecasting result in the next six hours.

• Journal of Korea Water Resources Association
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• v.52 no.2
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• pp.107-113
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• 2019

The flood forecasting model currently used in Korea calculates the runoff of basin using the lumped rainfall-runoff model and estimates the river level using the river and reservoir routing models. The lumped model assumes homogeneous drainage zones in the basin. Therefore, it can not consider various spatial characteristics in the basin. In addition, the rainfall data used in lumped model also has the same limitation because of using the point scale rainfall data. To overcome the limitations as mentioned above, many researchers have studied to apply the distributed rainfall-runoff model to flood forecasting system. In this study, to apply the Grid-based Rainfall-Runoff Model (GRM) to the Korean flood forecasting system, the optimal resolution is determined by analyzing the difference of the results of the runoff according to the various resolutions. If the grid size is to small, the computation time becomes excessive and it is not suitable for applying to the flood forecasting model. Even if the grid size is too large, it does not fit the purpose of analyzing the spatial distribution by applying the distributed model. As a result of this study, the optimal resolution which satisfies the accuracy of the bsin runoff prediction and the calculation speed suitable for the flood forecasting was proposed. The accuracy of the runoff prediction was analyzed by comparing the Nash-Sutcliffe model efficiency coefficient (NSE). The optimal resolution estimated from this study will be used as basic data for applying the distributed rainfall-runoff model to the flood forecasting system.