• Journal of Information Technology Applications and Management
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• 제14권4호
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• pp.139-158
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• 2007

Information sharing is key to effective supply chain management. In reality, however, it is impossible to get perfect information. Accordingly, only uncertain information can be accessed in business environment, and thus it is important to deal with the uncertainties of information in managing supply chains. This study adopts meteorological forecast as a typical uncertain information. The meteorological events may affect the demands for various weather-sensitive goods, such as beer, ices, clothes, electricity etc. In this study, a beer distribution game is modified by introducing meterological forecast information provided in a probabilistic format. The behavior patterns of the modified beer supply chains are investigated. for two conditions using the weather forecast with or without an information sharing. A value score is introduced to generalize the well-known performance measures employed in the study of supply chains, i.e.. inventory, backlog, and deviation of orders. The simulation result showed that meterological forecast information used in an information sharing environment was more effective than without information sharing, which emphasizes the synergy of uncertain information added to the information sharing environment.

• 한국태양에너지학회 논문집
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• 제39권2호
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• pp.71-80
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• 2019

Daily ahead forecast is necessary for the electricity balance between load and supply due to the variability renewable energy. Numerical weather prediction is usually employed to produce the solar irradiance as well as electric power forecast for more than 12 hours forecast horizon. UM-LDAPS model is the numerical weather prediction operated by Korea Meteorological Administration and it generates the 36 hours forecast of hourly total irradiance 4 times a day. This study attempts to evaluate the model performance against the in situ measurements at 37 ground stations from January to May, 2013. Relative mean bias error, mean absolute error and root mean square error of hourly total irradiance are averaged over all ground stations as being 8.2%, 21.2% and 29.6%, respectively. The behavior of mean bias error appears to be different; positively largest in Chupoongnyeong station but negatively largest in Daegu station. The distinct contrast might be attributed to the limitation of microphysics parameterization for thick and thin clouds in the model.

• 대기
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• 제25권1호
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• pp.1-18
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• 2015

The most objective way to overcome the limitation of numerical weather prediction model is to represent the uncertainty of prediction by introducing probabilistic forecast. The uncertainty of the numerical weather prediction system developed due to the parameterization of unresolved scale motions and the energy losses from the sub-scale physical processes. In this study, we focused on the growth of model errors. We performed ensemble forecast to represent model uncertainty. By employing the multi-physics scheme (PHYS) and the stochastic kinetic energy backscatter scheme (SKEBS) in simulating typhoon Rusa (2002), we assessed the performance level of the two schemes. The both schemes produced better results than the control run did in the ensemble mean forecast of the track. The results using PHYS improved by 28% and those based on SKEBS did by 7%. Both of the ensemble mean errors of the both schemes increased rapidly at the forecast time 84 hrs. The both ensemble spreads increased gradually during integration. The results based on SKEBS represented model errors very well during the forecast time of 96 hrs. After the period, it produced an under-dispersive pattern. The simulation based on PHYS overestimated the ensemble mean error during integration and represented the real situation well at the forecast time of 120 hrs. The displacement speed of the typhoon based on PHYS was closest to the best track, especially after landfall. In the sensitivity tests of the model uncertainty of SKEBS, ensemble mean forecast was sensitive to the physics parameterization. By adjusting the forcing parameter of SKEBS, the default experiment improved in the ensemble spread, ensemble mean errors, and moving speed.

• 대기
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• 제30권3호
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• pp.237-248
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• 2020

In order to effectively prepare for damage caused by weather events, it is important to proactively identify the possible impacts of weather phenomena on the domestic society and economy. Text mining and Network analysis are used in this paper to build a database of damage types and levels caused by heat wave. We collect news articles about heat wave from the SBS news website and determine the primary and secondary effects of that through network analysis. In addition to that, based on the frequency with which each impact keyword is mentioned, we estimate how much influence each factor has. As a result, the types of impacts caused by heat wave are efficiently derived. Among these types of impacts, we find that people in South Korea are mainly interested in algae and heat-related illness. Since this technique of analysis can be applied not only to news articles but also to social media contents, such as Twitter and Facebook, it is expected to be used as a useful tool for building weather impact databases.

• 전자공학회논문지
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• 제53권1호
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• pp.71-78
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• 2016

본 논문은 인공 신경망에 기반을 둔 새로운 전력 수요 예측 모델을 제시한다. 인공 신경망 입력 변수로 시간과 날씨요소를 고려하였다. 시간 요소는 하절기와 동절기 전력수요 데이터의 자기 상관계수를 측정하여 선정하였고, 날씨요소는 피어슨 상관계수를 이용하여 선정하였다. 중요한 날씨요소로는 온도와 이슬점으로 이들은 전력수요와 밀접한 상관관계를 가지고 있다. 반면에 습도, 기압, 풍속 등과 같은 날씨요소는 전력수요와의 상관관계가 높지 않게 나타나 신경망의 입력 변수에서 제외하였다. 실험결과 새로이 제안한 인공 신경망을 이용한 전력수요 모델은 시간요소 및 날씨요소와 이에 대한 가중치를 피크 전력율과 계절에 따라 차등 적용하여 높은 적중률을 보였다.

• 한국멀티미디어학회논문지
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• 제19권8호
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• pp.1530-1537
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• 2016

Recently, solar power generation shows the significant growth in the renewable energy field. Using the short-term prediction, it is possible to control the electric power demand and the power generation plan of the auxiliary device. However, a short-term prediction can be used when you know the weather forecast. If it is not possible to use the weather forecast information because of disconnection of network at the island and the mountains or for security reasons, the accuracy of prediction is not good. Therefore, in this paper, we proposed a system capable of short-term prediction of solar power generation amount by using only the weather information that has been collected by oneself. We used temperature, humidity and insolation as weather information. We have applied a moving average to each information because they had a characteristic of time series. It was composed of min, max and average of each information, differences of mutual information and gradient of it. An artificial neural network, SVM and RBF Network model was used for the prediction algorithm and they were combined by Ensemble method. The results of this suggest that using a moving average during pre-processing and ensemble prediction models will maximize prediction accuracy.

• International Journal of Advanced Culture Technology
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• 제6권4호
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• pp.190-194
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• 2018

The runway visual range is one of the important factors that decide the possibility of taking offs and landings of the airplane at local airports. The runway visual range is affected by weather conditions like fog, wind, etc. The pilots and aviation related workers check a local weather forecast such as runway visual range for safe flight. However there are several local airfields at which no other forecasting functions are provided due to realistic problems like the deterioration, breakdown, expensive purchasing cost of the measurement equipment. To this end, this study proposes a prediction model of runway visual range for a local airport by applying convolutional neural network that has been most commonly used for image/video recognition, image classification, natural language processing and so on to the prediction of runway visual range. For constituting the prediction model, we use the previous time series data of wind speed, humidity, temperature and runway visibility. This paper shows the usefulness of the proposed prediction model of runway visual range by comparing with the measured data.

• 디지털콘텐츠학회 논문지
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• 제17권3호
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• pp.157-164
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• 2016

태양광 발전량은 날씨에 큰 영향을 받는다. 기상 예보를 사용할 수 있는 환경이라면, 기상 예보 정보를 사용하여 미래의 태양광 발전량을 단기예측 할 수 있다. 하지만, 섬이나 산과 같이 네트워크의 단절에 의해 기상예보 정보를 사용할 수 없는 제한된 환경에서는 기상예보를 사용한 태양광 발전량 예측 모델을 사용할 수 없다. 따라서 본 논문에서는 시스템 자체적으로 수집할 수 있는 정보만을 이용하여 태양광 발전량을 단기 예측할 수 있는 시스템을 제안하였다. 예측의 정확도를 높이기 위하여 이전 온도정보와 발전량 정보를 이용하여 단기 예측모델을 생성하였다. 실험을 통하여 실데이터에 제안한 예측 모델을 적용하여 유용한 결과를 보였다.

• 한국습지학회지
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• 제5권1호
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• pp.29-40
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• 2003

This study was performed to research the relation between airmass thunderstorm and stability index with 12 years meteorological data(1990~2001) at Busan. Also We used the analysed stability indices from University of Wyoming to consider airmass thunderstorm. The frequency of thunderstorm occurrence during 12 years was 156 days(annual mean 13days). The airmass thunderstorm frequency was 14 days, most of those occurrence were summertime(59%). And occurrence hour of airmass thunderstorm was distributed from 1300LST to 2100LST broadly. The highest forecast index for airmass thunderstorm at Busan was K index, the lowest forecast index was SWEAT index. The forecasting of thunderstorms is based primary on the concepts of conditional instability, convective instability, and forced lifting of air near the surface. Instability is a critical factor in severe weather development. Severe weather stability indices can be a useful tool when applied correctly to a given convective weather situation.

• 대기
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• 제24권3호
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• pp.391-402
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• 2014

This study investigated the performances of precipitable water vapor (PWV) retrieval from the sets of ground global positioning system (GPS) signals, each of which had different length of observing-session duration, for the purpose of obtaining as short session duration as possible that is required at the least for appropriate retrieval of the PWV for meteorological usage. The shorter duration is highly desirable to make the most use of the GPS instrument on board the mobile observation vehicle making measurements place by place. First, using Bernese 5.0 software the PWV retrieval was conducted with the data sets of GPS signals archived continuously in 30 seconds interval during 2-month period of January and February, 2012 at Bukgangneung site. Each of the PWVs produced independently using different session durations was compared to that of radio-sonde launched at the same GPS location, a Bukgangneung site. Second, the same procedure was done using the data sets obtained from the mobile observation vehicle that was operating at Boseong area in Jeonnam province during Changma observation campaign in 2013, and the results were compared to that at Bukgangneung site. The results showed that as the observing-session duration increased the retrieval errors decreased with the dramatic change happening between 3 and 4 hours of the duration. On average, the root mean square error (RMSE) of the retrieved PWV was around 1 mm for the durations of greater than 4 hours. The results at both the Bukgangneung (fixed site) and Boseong (mobile vehicle) seemed to be fairly comparable with each other. From this study it is believed that at least 4 hours of observing-session duration is needed for the retrieval of PWV from the ground GPS for meteorological usage using Bernese 5.0 software.