• Korean Management Science Review
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• v.26 no.1
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• pp.77-91
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• 2009

It is the most important sucess factor for the electricity generation industry to minimize operations cost of surplus electricity generation through accurate demand forecasts. Temperature forecast is a significant input variable, because power demand is mainly linked to the air temperature. This study estimates the information value of the temperature forecast by analyzing the relationship between electricity load and daily air temperature in Korea. Firstly, several characteristics was analyzed by using a population-weighted temperature index, which was transformed from the daily data of the maximum, minimum and mean temperature for the year of 2005 to 2007. A neural network-based load forecaster was derived on the basis of the temperature index. The neural network then was used to evaluate the performance of load forecasts for various types of temperature forecasts (i.e., persistence forecast and perfect forecast) as well as the actual forecast provided by KMA(Korea Meteorological Administration). Finally, the result of the sensitivity analysis indicates that a $0.1^{\circ}C$ improvement in forecast accuracy is worth about $11 million per year.

• Fashion & Textile Research Journal
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• v.19 no.3
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• pp.289-295
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• 2017

This study aims to develop clothing sales forecast system using weather information. As the annual temperature variation affects changes in daily sales of seasonal clothes, sales period can be predicted growth, peak and decline period by changes of temperature. From this perspective, we analyzed the correlation between temperature and sales. Moving average method was applied in order to indicate long-term trend of temperature and sales changes. 7-day moving average temperature at the start/end points of the growth, peak, and decline period of S/S clothing sales was calculated as a reference temperature for sales forecast. According to the 2013 data analysis results, when 7-day moving average temperature value becomes $4^{\circ}C$ or higher, the growth period of S/S clothing sales starts. The peak period of S/S clothing sales starts at $17^{\circ}C$, up to the highest temperature. When temperature drops below $21^{\circ}C$ after the peak temperature, the decline period of S/S clothing sales is over. The reference temperature was applied to 2014 temperature data to forecast sales period. Through comparing the forecasted sales periods with the actual sales data, validity of the sales forecast system has been verified. Finally this study proposes 'clothing sales forecast system using weather information' as the method of clothing sales forecast.

• Atmosphere
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• v.28 no.4
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• pp.509-520
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• 2018

The performance of the newly designed Pusan National University Coupled General Circulation Model (PNU CGCM) Ensemble Forecast System which produce 40 ensemble members for 12-month lead prediction is evaluated and analyzed in terms of boreal winter temperature over South Korea (S. Korea). The influence of ensemble size on prediction skill is examined with 40 ensemble members and the result shows that spreads of predictability are larger when the size of ensemble member is smaller. Moreover, it is suggested that more than 20 ensemble members are required for better prediction of statistically significant inter-annual variability of wintertime temperature over S. Korea. As for the ensemble average (ENS), it shows superior forecast skill compared to each ensemble member and has significant temporal correlation with Automated Surface Observing System (ASOS) temperature at 99% confidence level. In addition to forecast skill for inter-annual variability of wintertime temperature over S. Korea, winter climatology around East Asia and synoptic characteristics of warm (above normal) and cold (below normal) winters are reasonably captured by PNU CGCM. For the categorical forecast with $3{\times}3$ contingency table, the deterministic forecast generally shows better performance than probabilistic forecast except for warm winter (hit rate of probabilistic forecast: 71%). It is also found that, in case of concentrated distribution of 40 ensemble members to one category out of the three, the probabilistic forecast tends to have relatively high predictability. Meanwhile, in the case when the ensemble members distribute evenly throughout the categories, the predictability becomes lower in the probabilistic forecast.

• Journal of the Korean Geographical Society
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• v.42 no.1 s.118
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• pp.1-14
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• 2007

This study aimed to evaluate weather forecast accuracies of minimum temperature, maximum temperature, precipitation and sky cover by the localized land forecast areas over South Korea Average forecast accuracy score of precipitation was the lowest while that of sky cover was the highest during the study period Overall forecast accuracy scores for Gangwon-do was the lowest while those for Gyeongsangnam-do and Gyeongsangbuk-do were higher than other areas. The frequencies of perfect forecast(eight points) by seasons, were the highest during winter and the lowest during summer. pressure pattern analyses for days when forecast accuracy scores were poor, showed that precipitation forecast accuracy scores were lower due to the movement of the stationary fronts during summers. When continental polar air masses expanded, forecast accuracy of temperature became greatly lower during autumns and winters As the migratory anticyclone pattern rapidly moved, forecast accuracy became lower during springs and autumns. Forecast accuracies were compared by wind directions at 850hPa for the Young-dong region where forecast accuracy was the lowest. Forecast accuracy scores on minimum and maximum temperatures were low when winds were westerlies and forecast accuracy scores of precipitation were low when winds were easterlies.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1689-1690
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• 2008

In this paper, The method of a temperature rasing forecast is suggested and simulated. The data used in this simulations exists in the KD Power and it was obtain by real transformer. The method of temperature forecast is based on a top oil temperature rising modeling which is proposed by the IEEE journal. We propose modifications of a modeling that accurately predicts a future transformer temperature. This Method is verified by simulations.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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• pp.2061-2065
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• 2008

In the conventional operation of ice-storage system based on operator's experience and judgement, the failure in forecast of cooling load occurs frequently due to operator's misjudgement and unskilled operation. This study presents the method of constructing self-organizing fuzzy models which forecast tomorrow temperature, humidity and cooling load periodically for economic and efficient operation of ice-storage system. To check the effectiveness and feasibility of the suggested algorithm, the actual example for forecasting temperature, humidity and cooling load of ice- storage system in KEPCO training institute, Sokcho, is examined. The computer simulation results show that the accuracy of temperature, humidity, cooling load forecast of the suggested algorithm is higher than that of the conventional methods.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.11
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• pp.948-953
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• 2002

The electric power load during the peak time in summer is strongly affected by cooling load, which decreases the preparation ratio of electricity and brings about the failure in the supply of electricity in the electric power system. The ice-storage system and heat pump system are possible solutions to settle this problem. In this study. the method of estimating temperature and humidity to forecast the cooling load of ice-storage system is suggested, then the method of forecasting the cooling load using fuzzy logic is suggested by simulating that the cooling load is calculated using actual temperature and humidity. The forecast of the temperature, humidity and cooling load are simulated, and it is shown that the forecasted data approach to the actual data. Operating the ice-storage system by the forecast of cooling load with night electric power will improve the ice-storage system efficiency and reduce the peak electric power load during the summer season as a result.

• Atmosphere
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• v.18 no.3
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• pp.237-248
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• 2008

A thermal index which considers metabolic heat generation of human body is proposed for operational forecasting. The new thermal index, Perceived Temperature (PT), is forecasted using Weather Research and Forecasting (WRF) mesoscale model and validated. Forecasted PT shows the characteristics of diurnal variation and topographic and latitudinal effect. Statistical skill scores such as correlation, bias, and RMSE are employed for objective verification of PT and input meteorological variables which are used for calculating PT. Verification result indicates that the accuracy of air temperature and wind forecast is higher in the initial forecast time, while relative humidity is improved as the forecast time increases. The forecasted PT during 2007 summer is lower than PT calculated by observation data. The predicted PT has a minimum Root-Mean-Square-Error (RMSE) of $7-8^{\circ}C$ at 9-18 hour forecast. Spatial distribution of PT shows that it is overestimated in western region, while PT in middle-eastern region is underestimated due to strong wind and low temperature forecast. Underestimation of wind speed and overestimation of relative humidity have caused higher PT than observation in southern region. The predicted PT from the mesoscale model gives appropriate information as a thermal index forecast. This study suggests that forecasted PT is applicable to the prediction of health warning based on the relationship between PT and mortality.

• Atmosphere
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• v.26 no.1
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• pp.59-72
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• 2016

The comparison of prediction errors in geopotential height, temperature, and precipitation forecasts is made quantitatively to evaluate medium-range forecast skills between Global Seasonal Forecasting System version 5 (GloSea5) and Unified Model (UM) in operation by Korea Meteorological Administration during 2014. In addition, the performances in prediction of sea surface temperature anomaly in NINO3.4 region, Madden and Julian Oscillation (MJO) index, and tropical storms in western north Pacific are evaluated. The result of evaluations appears that the forecast skill of UM with lower values of root-mean square error is generally superior to GloSea5 during forecast periods (0 to 12 days). The forecast error tends to increase rapidly in GloSea5 during the first half of the forecast period, and then it shows down so that the skill difference between UM and GloSea5 becomes negligible as the forecast time increases. Precipitation forecast of GloSea5 is not as bad as expected and the skill is comparable to that of UM during 10-day forecasts. Especially, in predictions of sea surface temperature in NINO3.4 region, MJO index, and tropical storms in western Pacific, GloSea5 shows similar or better performance than UM. Throughout comparison of forecast skills for main meteorological elements and weather extremes during medium-range, the effects of initial and model errors in atmosphere-ocean coupled model are verified and it is suggested that GloSea5 is useful system for not only seasonal forecasts but also short- and medium-range forecasts.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.21 no.1
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• pp.68-73
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• 2013

This study is to make a protype of forecast guidance for forecasters from analyzing the characteristics of Korea Fog. The trend of Korea fog showed the decline in the number of foggy days and the duration time, the gradient is -1.24days/year under 3 miles and -0.98days/year under 1 mile and -1.64hours/year under 3 miles and -3.18hours/year under 1 mile in duration time in 27 ROKAF base. To find the protype of inland and coastal forecast guidance, Daegu base as a representation of the inland base and Gangneung base as the representation of the coastal base were chosen. For Daegu base, the mixture of relative humidity, sky condition, and the position of high pressure were selected for the forecast guidance. For Gangneung base, pressure pattern, sea surface temperature, sea currents, and 850hPa temperature patterns were selected for the forecast guidance.