• 한국태양에너지학회 논문집
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• 제36권1호
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• pp.27-37
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• 2016

A simple but practical Ensemble Prediction System(EPS) for wind power forecasting was developed and evaluated using the measurement of the offshore meteorological tower, HeMOSU-1(Herald of Meteorological and Oceanographic Special Unite-1) installed at the Southwest Offshore in South Korea. The EPS developed by the Korea Institute of Energy Research is based on a simple ensemble mean of two Numerical Weather Prediction(NWP) models, WRF-NMM and WRF-ARW. In addition, the Kalman Filter is applied for real-time quality improvement of wind ensembles. All forecasts with EPS were analyzed in comparison with the HeMOSU-1 measurements at 97 m above sea level during Typhoon Bolaven episode in August 2012. The results indicate that EPS was in the best agreement with the in-situ measurement regarding (peak) wind speed and cut-out speed incidence. The RMSE of wind speed was 1.44 m/s while the incidence time lag of cut-out wind speed was 0 hour, which means that the EPS properly predicted a development and its movement. The duration of cut-out wind speed period by the EPS was also acceptable. This study is anticipated to provide a useful quantitative guide and information for a large-scale offshore wind farm operation in the decision making of wind turbine control especially during a typhoon episode.

• 대한조선학회논문집
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• 제56권6호
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• pp.523-531
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• 2019

The low birthrate and the need for national defense reform in Korea drive the Navy to develop efficient human resource planning such as a manpower forecasting model. However, to our knowledge, there is no study exploring the manpower forecasting model for naval ships in Korea. The purpose of this paper is to develop a model for forecasting manpower demand in naval ships. Data for analyses were drawn from 19 ships in the Korean Navy. Results indicate that mission type is significantly related to the number of manpower. Specifically, battleships need the more manpower than the battle support ships. The results also showed that the weight of hull structure-engine and the weight of the weapons system significantly increased the number of manpower. However, the weight of the combat system was not significant. In addition, whereas the automation level of hull structure-engine and the automation level of weapon system was found to be negatively related to the number of manpower, the automation level of combat system was positively related to it. The model developed here contributes to an advanced human resource planning of the Korean Navy. Implications, limitations, and directions for future research are discussed.

• KNOM Review
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• 제22권3호
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• pp.25-30
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• 2019

기상 응용의 경우 시간적, 자원적 한계 내에서도 의미 있는 결과를 도출해 제공해야 한다. 수많은 과거 데이터를 통한 예보는 시간적인 소요가 크며, 국지성 태풍 예보와 같은 재난 안전 관련 분석/예측의 경우에는 여전히 자원적 한계가 존재한다. 태풍 예보, 도로별 침수/홍수 지역 예측 서비스 등 시간 제약하에 결과를 도출해야 하는 경우와 제한적인 물리적 환경 조건으로 인해 발생하는 문제 없이 적합한 예보를 제공해야 한다. 본 논문에서는 시간적, 자원적 조건에서도 원활한 예보 서비스 제공을 위해 기상 및 기후 예측 응용을 분석한다. 격자 크게 따른 수행 시간 분석을 통해 격자 조절을 통해 시간적 제약 조건이 있는 경우에 대처할 수있음을 확인하였다. 또한 메모리 자원 조절을 통해 수행 시간을 분석하여 성능에 영향을 미치지 않는 최소 자원 조건을 확인하였으며 swap, mlock 분석을 통해 응용의 자원 사용 패턴을 확인하였다.

• 디지털산업정보학회논문지
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• 제7권2호
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• pp.147-159
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• 2011

In this paper the demand and supply of labor are forecasted over the next 10 years for customized nurturing human resource focused on Manufacturing Industry in Chung-Nam Province. Despite that the industrial structure is rapidly changing, industrial labors are nurturing on the basis of past industrial structure. This research is conducted for reducing mismatched labors throughout forecasting human resources until 2020. As a practical approach, the BLS Methodology is partially utilized. And the previous researches and official statistics data are reviewed. In conclusion, this study presents that more human resources on Manufacturing Industry than other Industries will be needed in Chung-Nam province. In details, it shows that there will be required more Industrial labors for strategic industries for examples, Audio and Video related industry, and Car related industry which is propelling by overall local government. In additions, policy implications are developed by analyzing current status and forecasting the labor demand and supply in the Chung-Nam Manufacturing sector.

• 한국시뮬레이션학회논문지
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• 제12권4호
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• pp.83-94
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• 2003

Managing a call center is a complex and diverse challenge. Call center becomes a very important contact point and a data ware house for successful CRM. Improving performance of call center is critical and valuable for providing better service. In this study we applied forecasting technique to estimate incoming calls and ProModel based simulation model to enhance performance of a mobile telecommunication company's call center. The simulation study shows reduction in managing cost and better customer's satisfaction.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제11권8호
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• pp.339-346
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• 2022

최근 화석연료의 무분별한 사용으로 인한 자원고갈 문제 및 기후변화 문제 등이 심각해짐에 따라 화석연료를 대체할 수 있는 신재생에너지에 대한 관심이 증가하고 있다. 특히 신재생에너지 중 태양광 에너지는 다른 신재생에너지원에 비해 고갈될 염려가 적고, 공간적인 제약이 크지 않아 전국적으로 수요가 증가하고 있다. 태양광 발전 시스템에서 생산된 전력을 효율적으로 사용하기 위해서는 보다 정확한 태양광 발전량 예측 모델이 필요하다. 이를 위하여 다양한 기계학습 및 심층학습 기반의 태양광 발전량 예측 모델이 제안되었지만, 심층학습 기반의 예측 모델은 모델 내부에서 일어나는 의사결정 과정을 해석하기가 어렵다는 단점을 보유하고 있다. 이러한 문제를 해결하기 위하여 설명 가능한 인공지능 기술이 많은 주목을 받고 있다. 설명 가능한 인공지능 기술을 통하여 예측 모델의 결과 도출 과정을 해석할 수 있다면 모델의 신뢰성을 확보할 수 있을 뿐만 아니라 해석된 도출 결과를 바탕으로 모델을 개선하여 성능 향상을 기대할 수도 있다. 이에 본 논문에서는 BiLSTM(Bidirectional Long Short-Term Memory)을 사용하여 모델을 구성하고, 모델에서 어떻게 예측값이 도출되었는지를 SHAP(SHapley Additive exPlanations)을 통하여 설명하는 설명 가능한 태양광 발전량 예측 기법을 제안한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권11호
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• pp.3584-3602
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• 2022

NFV realizes flexible and rapid software deployment and management of network functions in the cloud network, and provides network services in the form of chained virtual network functions (VNFs). However, using VNFs to provide quality guaranteed services is still a challenge because of the inherent difficulty in intelligently scaling VNFs to handle traffic fluctuations. Most existing works scale VNFs with fixed-capacity instances, that is they take instances of the same size and determine a suitable deployment location without considering the cloud network resource distribution. This paper proposes a traffic forecasted assisted proactive VNF scaling approach, and it adopts the instance capacity adaptive to the node resource. We first model the VNF scaling as integer quadratic programming and then propose a proactive adaptive VNF scaling (PAVS) approach. The approach employs an efficient traffic forecasting method based on LSTM to predict the upcoming traffic demands. With the obtained traffic demands, we design a resource-aware new VNF instance deployment algorithm to scale out under-provisioning VNFs and a redundant VNF instance management mechanism to scale in over-provisioning VNFs. Trace-driven simulation demonstrates that our proposed approach can respond to traffic fluctuation in advance and reduce the total cost significantly.

• 한국환경과학회지
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• 제22권8호
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• pp.965-977
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• 2013

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. It is necessary to get probabilistic forecasts to establish risk-based reservoir operation policies. Probabilistic forecasts may be useful for the users who assess and manage risks according to decision-making responding forecasting results. Probabilistic forecasting of seasonal inflow to Andong dam is performed and assessed using selected predictors from sea surface temperature and 500 hPa geopotential height data. Categorical probability forecast by Piechota's method and logistic regression analysis, and probability forecast by conditional probability density function are used to forecast seasonal inflow. Kernel density function is used in categorical probability forecast by Piechota's method and probability forecast by conditional probability density function. The results of categorical probability forecasts are assessed by Brier skill score. The assessment reveals that the categorical probability forecasts are better than the reference forecasts. The results of forecasts using conditional probability density function are assessed by qualitative approach and transformed categorical probability forecasts. The assessment of the forecasts which are transformed to categorical probability forecasts shows that the results of the forecasts by conditional probability density function are much better than those of the forecasts by Piechota's method and logistic regression analysis except for winter season data.

• 한국환경과학회지
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• 제22권8호
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• pp.953-963
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• 2013

Reliable long-term streamflow forecasting is invaluable for water resource planning and management which allocates water supply according to the demand of water users. Forecasting of seasonal inflow to Andong dam is performed and assessed using statistical methods based on hydrometeorological data. Predictors which is used to forecast seasonal inflow to Andong dam are selected from southern oscillation index, sea surface temperature, and 500 hPa geopotential height data in northern hemisphere. Predictors are selected by the following procedure. Primary predictors sets are obtained, and then final predictors are determined from the sets. The primary predictor sets for each season are identified using cross correlation and mutual information. The final predictors are identified using partial cross correlation and partial mutual information. In each season, there are three selected predictors. The values are determined using bootstrapping technique considering a specific significance level for predictor selection. Seasonal inflow forecasting is performed by multiple linear regression analysis using the selected predictors for each season, and the results of forecast using cross validation are assessed. Multiple linear regression analysis is performed using SAS. The results of multiple linear regression analysis are assessed by mean squared error and mean absolute error. And contingency table is established and assessed by Heidke skill score. The assessment reveals that the forecasts by multiple linear regression analysis are better than the reference forecasts.

• 한국컴퓨터정보학회논문지
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• 제7권1호
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• pp.94-102
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• 2002

본 논문에서는 국내 전파자원 관리를 위한 전파자원의 중장기 수요예측 방법론 개발의 전 단계로서 전파자원, 즉 주파수의 수요예측 방법론을 제시한다. 특히, 기존 공급자 위주의 하향식(Top-down) 개념의 수요예측 방법론이 아니라 무선자원을 실질적으로 소비하는 사용자를 중심으로 하는 상향식(Bottom-up) 개념의 주파수 수요예측 모형을 제시한다. 이는 크게 서비스 정의, 서비스 특성 분류, 서비스별 대표 속성 도출, 서비스 수요예측, 전파자원과의 매핑 검증 및 주파수 수요예측의 7단계로 이루어지며 각 단계에서 수행하여야 할 주요 업무를 설명한다. 아울러 PCS개인통신환경에서의 주파수 소요량 산출 예를 제시함으로서 개발된 모형의 타당성을 입증한다.