• 한국항만경제학회지
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• 제36권2호
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• pp.53-62
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• 2020

해운항만물류산업은 세계 경제활동과 밀접한 관계를 가지고 있으며, 특히 무역의존도가 높은 우리나라의 항만 시설은 중요한 사회간접자본시설이다. 부산항은 우리나라 최대의 항만으로 우리나라 컨테이너 운송의 75%가 부산항을 통해 운송되고 있으며, 국가 경쟁력 측면에서 그 중요성은 매우 크다. 항만 물동량 예측은 항만 개발 및 운영 전략에 영향을 미치며, 정확도 높은 컨테이너 물동량 예측은 필수적이다. 하지만 오늘날 해운항만물류산업 환경의 급격한 변화로 인해 기존 시계열 예측 방법으로는 예측 정확도 향상에 어려움이 있다. 본 연구에서는 부산항 컨테이너 물동량 예측 정확도 향상을 위해 딥러닝 모형 중 LSTM 모형을 활용하여 컨테이너 물동량을 예측한다. 모형의 성능 평가를 위해서 SARIMA 모형과 LSTM 모형의 예측 정확도를 비교한다. 그 결과 LSTM 모형이 SARIMA 모형보다 예측 정확도가 높게 나타났으며, 예측치가 실측치의 특성을 반영하여 잘 나타나고 있음을 확인하였다.

• 조명전기설비학회논문지
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• 제15권6호
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• pp.35-42
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• 2001

1일 최대전력 부하 예측 자료는 계통의 경제적 운용과 전력 감시에 필수적이므로 정확한 예측기법이 요구된다. 신경회로망이나 퍼지이론을 한 예측비법의 장점은 정도(精度)가 높고 운용하기가 편리한 점은 있으나 학습시간이 길고, 부하가 급변할 때는 예측오차가 크게 발생한다. 본 연구에서는 이러한 단점을 개선하기 위하여 새로운 예측 기법을 제시하였으며 예측결과에서 타당성이 입증되었다.

• 전기학회논문지
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• 제59권6호
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• pp.1041-1045
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• 2010

Short-term electric load forecasting of power systems is essential for the power system stability and the efficient power system operation. An accurate load forecasting scheme improves the power system security and saves some economic losses in power system operations. Due to scarcity of the historical same type of holiday load data, most big electric load forecasting errors occur on load forecasting for the holidays. The fuzzy linear regression model has showed good accuracy for the load forecasting of the holidays. However, it is not good enough to forecast the load of the election day. The concept of the load variation rate for the load forecasting of the election day is introduced. The proposed algorithm shows its good accuracy in that the average percentage error for the short-term 24 hourly loads forecasting of the election days is 2.27%. The accuracy of the proposed 24 hourly loads forecasting of the election days is compared with the fuzzy linear regression method. The proposed method gives much better forecasting accuracy with overall average error of 2.27%, which improved about average error of 2% as compared to the fuzzy linear regression method.

• 물과 미래
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• 제3권2호
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• pp.50-53
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• 1970

• 한국국방경영분석학회지
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• 제4권1호
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• pp.57-68
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• 1978

In forecasting the demand rate of repair parts for old tank types under the limited historical data, this reportan alyzes which techniques give the smallest forecasting error, also economic repair limits and the return for additional repair costs are formulated as a mathematical model

• 대한전기학회논문지:전력기술부문A
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• 제50권7호
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• pp.303-308
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• 2001

This paper presents algorithm to plan construction and expanding of substation considering contingency accidents by proposing utilization factor according to configuration of substation bank system. In this paper, firstly, proper sphere of supply area by each district which could be standardized with respect to its supply capacity is established under assumption of long term load forecasting. Secondly, goal of utilization ratio based on configuration of substation bank was set to keep reliability by remaining sound bank when it happen to one bank accidents. Finally, it is set up for optimal construction and expanding of substation considering economy and reliability simultaneously about substation to exceed these ratio. To verify proposed algorithm, at first, after adopting a part of Kangnam area in Seoul as area for testing, it is divided into several regions for this area according to power branches of power utility. Secondly, by deriving correlation factor between load demand and economic indicators in these region respectively, the regional load forecasting was performed with economic growth and city plan scenario. Finally, based on the predicted load demand by region and land use data which is identified from air-photographic, the load demand by district was predicted. Also, planning for substation considering contingency is formulated to expand taking into account computing utilization factor which is based on configuration of substation bank respectively.

• 농업과학연구
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• 제48권4호
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• pp.797-806
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• 2021

As the number of scaled-up ranches increased and agile responses to market changes became possible, decision-making by Hanwoo cattle farms also began to affect short-term shipments. Considering the changing environment of the Hanwoo supply market and the response speed of producers, it is necessary quickly to grasp the forecast ahead of time and to respond accordingly in an effort to stabilize supply and demand in the Hanwoo market. In this study, short-term forecasting model centered on the supply of Hanwoo was established. The analysis conducted here indicates that the slaughter of Hanwoo males increases by 0.248 as the number of beef cattle raised over 29 months of age in the previous month increases by one, and 0.764 Hanwoo females were slaughtered under average conditions for every Hanwoo male slaughtered. With regard to time, the slaughtering of Hanwoo was higher in January and August, which are months known for holiday food preparation activities for the New Year and Chuseok in Korea, respectively. Simulations indicated that errors were within 10% in all simulations performed through the Hanwoo supply model. Accordingly, it is considered that the estimation results from the supply model devised in this study are reliable and that the model has good structural stability.

• 응용통계연구
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• 제23권1호
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• pp.63-71
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• 2010

기업가들은 일반적으로 기업의 성장을 위하여 국내외 경제동향에 대하여 면밀한 분석과 판단 및 예측을 하고 기업의 경영 활동에 반영한다. 기업가들의 이와 같은 종합적인 판단, 예측, 계획 등은 생산, 투자, 고용 등 기업의 경제활동에 영향을 미치게 되며, 국민경제 전체의 경제활동 수준이라 할 수 있는 경기에도 큰 영향을 미치게 된다. 기업경기 실사지수(Business Survey Index; BSI)는 이러한 기업가의 주관적이고 심리적인 요인에 대한 정보를 수집하여 경기분석에 활용하고자 하는 필요성에 의해 작성되었다. 기업경기실사지수는 과거 외환위기를 전후한 경기변동기에서 경제예측을 위한 단기시계열 모형의 매우 유용한 변수로 이용되었다. 최근의 금융위기는 과거 외환위기 당시와 유사한 급격한 경기변동올 수반하연서 기업정기실사지수의 경제예측변수로서의 중요성을 재차 부각시졌다. 본고에서는 이와 같이 유용성이 높아지고 있는 경제심리지표로서 기업경기실사지수의 의미에 대해 개괄하고 동 지수에 영향을 미치고 있는 요인에는 어떠한 것들이 있는지 살펴보았다. 분석을 위해 GUIDE 회귀나무 알고리즘을 이용하였으며, 분석한 결과 다양한 경제변수틀 중 제조업 가동률 및 소비재 판매액 등 기업의 활동과 직결된 지표와 더불어 kospi와 환율 등 금융시장의 안정성과 관련된 지표도 경제심리에 영향을 미치는 변수로 나타났다.

• 농업과학연구
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• 제45권4호
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• pp.859-870
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• 2018

Dried red peppers are a staple agricultural product used in Korean cuisine and as such, are an important aspect of agricultural producers' income. Correctly forecasting both their supply and demand situations and price is very important in terms of the producers' income and consumer price stability. The primary objective of this study was to compare the performance of time series forecasting models for dried red peppers in Korea. In this study, three models (an autoregressive model with exogenous variables [ARX], AR-exponential generalized autoregressive conditional heteroscedasticity [EGARCH], and ARX-EGARCH) are presented for forecasting the wholesale price of dried red peppers. As a result of the analysis, it was shown that the ARX model and ARX-EGARCH model, each of which adopt both the rolling window and the adding approach and use the agricultural cooperatives price as the exogenous variable, showed a better forecasting performance compared to the autoregressive model (AR)-EGARCH model. Based on the estimation methods and results, there was no significant difference in the accuracy of the estimation between the rolling window and adding approach. In the case of dried red peppers, there is limitation in building the price forecasting models with a market-structured approach. In this regard, estimating a forecasting model using only price data and identifying the forecast performance can be expected to complement the current pricing forecast model which relies on market shipments.

• KIEE International Transactions on Power Engineering
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• 제4A권3호
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• pp.159-166
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• 2004

Forecasting prices in electricity markets is critical for consumers and producers in planning their operations and managing their price risk. We utilize the generalized autoregressive conditionally heteroskedastic (GARCH) method to forecast the electricity prices in two regions of New York： New York City and Central New York State. We contrast the one-day forecasts of the GARCH against techniques such as dynamic regression, transfer function models, and exponential smoothing. We also examine the effect on our forecasting of omitting some of the extreme values in the electricity prices. We show that accounting for the extreme values and the heteroskedactic variance in the electricity price time-series can significantly improve the accuracy of the forecasting. Additionally, we document the higher volatility in New York City electricity prices. Differences in volatility between regions are important in the pricing of electricity options and for analyzing market performance.