• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.271-274
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• 2005

Under competitive power markets, various long-term and short-term contracts based on spot price are used by producers and consumers. So an accurate forecasting for spot price allow market participants to develop bidding strategies in order to maximize their benefit. Artificial Neural Network is a powerful method in forecasting problem. In this paper we used Radial Basis Function(RBF) network to forecast spot price. To learn ANN, in addition to price history, we used some other effective inputs such as load level, fuel price, generation and transmission facilities situation. Results indicate that this forecasting method is accurate and useful.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.4
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• pp.83-92
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• 2022

Due to the uncertainty in the order of the integrated model, the SARIMA-LSTM model, SARIMA-SVR model, LSTM-SARIMA model, and SVR-SARIMA model are constructed respectively to determine the best-combined model for forecasting the China-Russia trade turnover. Meanwhile, the effect of the order of the combined models on the prediction results is analyzed. Using indicators such as MAPE and RMSE, we compare and evaluate the predictive effects of different models. The results show that the SARIMA-LSTM model combines the SARIMA model's short-term forecasting advantage with the LSTM model's long-term forecasting advantage, which has the highest forecast accuracy of all models and can accurately predict the trend of China-Russia trade turnover in the post-epidemic period. Furthermore, the SARIMA - LSTM model has a higher forecast accuracy than the LSTM-ARIMA model. Nevertheless, the SARIMA-SVR model's forecast accuracy is lower than the SVR-SARIMA model's. As a result, the combined models' order has no bearing on the predicting outcomes for the China-Russia trade turnover time series.

• Korea journal of population studies
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• v.8 no.1
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• pp.87-117
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• 1985

In Principle, the distriction should be understood between projections and forecasts. When the author or user of a projection is willing to describe it as indicating the most likely population at a give date, then he has made a forecast Population change since 1 960 has been reviewed briefly in order to forecast the population of Korea in the year 2,000 which is a leading factor in long term national development plan for which Korea Institute for Population and Health (KIPH) has been participated since 1983. The author of this paper introduced the population forecast prepared for the long term national development plan and an attempt of comparisons with other forecasts such as D.P. Smith's, T. Frejka's, Economic Planning Board's (EPB), UN's and S.B. Lee's was made. Those six forecasts of Korean future population in year 2,000 varried from 48.5 million to 50.0 million due to the base population and assumption of fertility and mortality however the range of total population size is not large enough. Taking four forecasts such as KIPH, EPB, UN, and Lee based on 1980 population census results and latest data of fertility and mortality, KIPH and UN forecast are close in total population size even though there was a slight difference in fertility and mortality assumptions. The smallest size of total population was shown by S.B. Lee (see Table 13) although the difference between KIPH and Lee was approximately one million which is two percent of total population in year 2,000. As a summary of conclusion the author pointed out that one can take anyone of forecasts prepared by different body because size and proportion wise of the Korean population until early I 990s can not be different much and new population projections must be provided by using 1985 population census data and other latest fertility and mortality information coflected by Korea Institute for Population and Health and Economic Planning Board in forth comming year.

• Journal of Korea Water Resources Association
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• v.41 no.7
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• pp.737-746
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• 2008

The purpose of developing a rainfall-runoff and reservoir model is to provide an analysis tool for hydrological engineers in order to forecast discharge of rivers and to accomplish reservoir operations easily and accurately. In this study, based on the short-term rainfall-runoff storage function model which has gained popularity for real time flood forecast in practical water management affairs, a long-term runoff model was developed for the improvement of the calculation method of effective rainfall and percolation at the infiltration area. Annual discharge was simulated for three dam watersheds(Andong, Hapcheon, Milyang) in Nakdong River basin to analyze the accuracy of the developed model and compare it to SSARR model, which is used as the long-term runoff model in current practical water management affairs. As the result of the comparison of hydrographs, SSARR model showed relatively better results. However, it is possible for the developed model to simulate reliable long-term runoff using relatively little available data and is useful for hydrological engineers in practical affairs.

• Journal of the Korean earth science society
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• v.45 no.4
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• pp.327-337
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• 2024

The ocean is linked to long-term climate variability, but there are very few methods to assess the short-term performance of forecast models. This study analyzes the short-term prediction performance regarding ocean temperature and salinity of the Global Seasonal prediction system version 5 (GloSea5). GloSea5 is a historical climate re-creation (2001-2010) performed on the 1st, 9th, 17th, and 25th of each month. It comprises three ensembles. High-resolution hindcasts from the three ensembles were compared with the Array for Real-Time Geostrophic Oceanography (ARGO) float data for the period 2001-2010. The horizontal position was preprocessed to match the ARGO float data and the vertical layer to the GloSea5 data. The root mean square error (RMSE), Brier Score (BS), and Brier Skill Score (BSS) were calculated for short-term forecast periods with a lead-time of 10 days. The results show that sea surface temperature (SST) has a large RMSE in the western boundary current region in Pacific and Atlantic Oceans and Antarctic Circumpolar Current region, and sea surface salinity (SSS) has significant errors in the tropics with high precipitation, with both variables having the largest errors in the Atlantic. SST and SSS had larger errors during the fall for the NINO3.4 region and during the summer for the East Sea. Computing the BS and BSS for ocean temperature and salinity in the NINO3.4 region revealed that forecast skill decreases with increasing lead-time for SST, but not for SSS. The preprocessing of GloSea5 forecasts to match the ARGO float data applied in this study, and the evaluation methods for forecast models using the BS and BSS, could be applied to evaluate other forecast models and/or variables.

• Environmental and Resource Economics Review
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• v.28 no.3
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• pp.437-465
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• 2019

In this study, we propose a new method to forecast long-term energy demand in Korea. Based on Chang et al. (2016), which models the time varying long-run relationship between electricity demand and GDP with a function coefficient panel model, we design several schemes to retain objectivity of the forecasting model. First, we select the bandwidth parameters for the income coefficient based on the out-of-sample forecasting performance. Second, we extend the income coefficient using the functional principal component analysis method. Third, we proposed a method to reflect the elasticity change patterns inherent in Korea. In the empirical analysis part, we forecasts the long-term energy demand in Korea using the proposed method to show that the proposed method generates more stable long term forecasts than the existing methods.

• Journal of Korea Water Resources Association
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• v.50 no.10
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• pp.691-701
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• 2017

The objective of this study is to evaluate the long-term drought outlook information based on long-term forecast data for the 2015 drought event. In order to estimate the Standardized Precipitation Index (SPI) for different durations (3-, 6-, 9-, 12-months), we used the observation precipitation of 59 Automated Synoptic Observing System (ASOS) sites, forecast and hindcast data of GloSea5. The Receiver Operating Characteristic (ROC) analysis and statistical analysis (Correlation Coefficient, CC; Root Mean Square Error, RMSE) were used to evaluate the utilization of drought outlook information for the forecast lead-times (1~6months). As a result of ROC analysis, ROC scores of SPI(3), SPI(6), SPI(9) and SPI(12) were estimated to be over 0.70 until the 2-, 3-, 4- and 5-months. The CC and RMSE values of SPI(3), SPI(6), SPI(9) and SPI(12) for forecast lead-time were estimated as (0.60, 0.87), (0.72, 0.95), (0.75, 0.95) and (0.77, 0.89) until the 2-, 4-, 5- and 6-months respectively.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2011.11a
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• pp.20-27
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• 2011

A system for fine-resolution long-range weather forecast is introduced in this study. The system is basically consisted of a global-scale coupled general circulation model (CGCM) and Weather Research and Forecast (WRF) regional model. The system makes use of a data assimilation method in order to reduce the initial shock or drift that occurs at the beginning of coupling due to imbalance between model dynamics and observed initial condition. The long-range predictions are produced in the system based on a non-linear ensemble method. At the same time, the model bias are eliminated by estimating the difference between hindcast model climate and observation. In this research, the predictability of the forecast system is studied, and it is illustrated that the system can be effectively used for the high resolution long-term weather prediction. Also, using the system, fine-resolution climatological data has been produced with high degree of accuracy. It is proved that the production of agrometeorological variables that are not intensively observed are also possible.

• Asia-Pacific Journal of Business
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• v.10 no.4
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• pp.65-75
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• 2019

We point out the limitations of Bloomberg Adjustment beta, shows that long-term beta does not converge with 1 and suggests an alternative to using proxy beta as beta's long-term forecast. We analyze whether the beta produced in the manner proposed by Bloomberg beta or proxy beta meets the purpose of calculating capital costs, for example, for the evaluation of corporate value. In particular, We apply in impairment valuations of assets and some analysis of how it affects. The proposal of the article applied in cases of analysis results are as follows : First, unlike the Bloomberg approach, long-term beta does not converge with market beta and therefore is not suitable as market forecast by beta. Second, estimating the suggested proxy beta as beta's predictive value resulted in Bloomberg beta and other adjustment Beta in the case categories, and the gap was large. Third, applying proxy beta results in a more appropriate valuation of the impairment loss on assets.

• Journal of Korea Water Resources Association
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• v.46 no.12
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• pp.1249-1263
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• 2013

In order to minimize the damages caused by long-term drought, appropriate drought management plans of the basin should be established with the drought forecasting technology. Further, in order to build reasonable adaptive measurement for future drought, the duration and severity of drought must be predicted quantitatively in advance. Thus, this study, attempts to forecast drought in Korea by using an Artificial Neural Network Model, and drought index, which are the representative statistical approach most frequently used for hydrological time series forecasting. SPI (Standardized Precipitation Index) for major weather stations in Korea, estimated using observed historical precipitation, was used as input variables to the MLP (Multi Layer Perceptron) Neural Network model. Data set from 1976 to 2000 was selected as the training period for the parameter calibration and data from 2001 to 2010 was set as the validation period for the drought forecast. The optimal model for drought forecast determined by training process was applied to drought forecast using SPI (3), SPI (6) and SPI (12) over different forecasting lead time (1 to 6 months). Drought forecast with SPI (3) shows good result only in case of 1 month forecast lead time, SPI (6) shows good accordance with observed data for 1-3 months forecast lead time and SPI (12) shows relatively good results in case of up to 1~5 months forecast lead time. The analysis of this study shows that SPI (3) can be used for only 1-month short-term drought forecast. SPI (6) and SPI (12) have advantage over long-term drought forecast for 3~5 months lead time.