• 한국항공운항학회지
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• 제28권4호
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• pp.147-153
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• 2020

This study analyzed the outlook for aviation demand for the recovery of the aviation industry, focusing on airlines facing difficulties in management due to the Covid-19 crisis. Although the timing of the recovery in aviation demand is uncertain at the moment, this study is based on prior research related to Covid-19 and forecasts by aviation specialists, and analyzed by SWOT technique to a group of aviation experts to derive and suggest implications for the prospects of aviation demand. Looking at the implications based on the analysis results, first, customer trust to prevent infection should be considered a top priority for recovering aviation demand. Second, promote reasonable air price policy. Finally, it seeks to try various research and analysis techniques to predict long-term aviation demand to overcome Covid-19.

• 자원ㆍ환경경제연구
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• 제28권3호
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• pp.437-465
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• 2019

본 연구에서는 장기 전력 수요와 GDP 사이의 소득계수를 시간과 GDP의 값에 따라 변화하도록 모형화한 Chang et al.(2016)에 기반을 두어 장기 에너지 수요의 예측에 관련된 새로운 방법을 제안한다. 본 논문에서는 장기 에너지와 GDP 사이의 소득계수를 함수로 표현하고, 함수 주성분 분석(Functional Principal Component Analysis)을 통하여 함수계수(Functional Coefficient)를 예측하고 이를 장기 에너지 수요 예측에 적용한다. 또한 함수계수를 비모수적으로 추정할 때 너비띠 모수를 예측 실험 오차를 최소화하도록 설정하는 방식을 제안하였고 개별 국가의 함수계수 변화 패턴을 반영하여 개별 국가의 특수성을 반영하는 예측 방법도 제시한다. 실증분석에서는 전 세계 에너지 데이터를 이용하여 한국의 장기 에너지 수요 예측을 본 논문에서 제시한 방법으로 예측하고, 기존의 방법들 보다 안정적인 장기 에너지 수요 예측이 가능함을 보였다.

• 상하수도학회지
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• 제21권1호
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• pp.75-82
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• 2007

When one forecasts urban water demand in a long-term, multivariate model can give more benefits than per capita requirement model. However, the former has shortcomings in that statistically high explanatory power cannot be obtained well, and change in customer behavior cannot be considered. If the past water consumption effects the future water demand, dynamic model may describe real water consumption data better than static model, i.e. the existing multivariate model. On these grounds, this study built dynamic model using system dynamics. From a case study in Seoul and Busan city, dynamic model was expected to forecast water demand more descriptively and reliably.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 B
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• pp.1260-1262
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• 2004

Direct Load Control(DLC) system is a load management program for stablization of electric power supply-demand. It is a series of acts limiting the demand of selected demand side at peak load or other time periods. Recently, power supply-demand instability due to dramatic increase in power usage such as summertime air-conditioning load has brought forecasts of decrease in power supply capability. Therefore heightening the load factor through systematic load management, in other words, Direct Load Control became necessary. By examining the composition and operation of the DLC system, this paper provides conceptional understanding of the DLC system and help in system research.

• 에너지공학
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• 제28권4호
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• pp.82-93
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• 2019

본 연구에서는 기존 열수요 예측 시스템이 공휴일과 같은 특정 일자의 열수요 예측율이 저하되는 문제점을 개선하기 위해 새로운 모델을 제안한다. 제안된 모델은 사계절 혼합형 신경망 모델(Four Season Mixed Heat Demand Prediction Neural Network Model)로서 열수요 예측율 상승하였고, 특히 예측일 유형별(평일/주말/공휴일) 열수요 예측율이 크게 증가하였다. 제안된 모델은 다음과 같은 과정을 통해 선정되었다. 특정 계절에 예측일 유형별로 고른 오차를 갖는 모델을 선정하여 전체 예측 모델을 구성한다. 학습 시간의 단축과 과도학습을 방지하기 위해 구조적으로 단순화된 서로 다른 4개의 모델을 각각 학습한 후에 다양한 조합을 통해 최적의 예측 오차를 보여주는 모델을 선정하였다. 모델의 출력은 예측일의 24시간의 시간대별 열수요이며 총합은 일일 총열수요이다. 이 예측값을 통해 효율적인 열공급 계획을 수립 할 수 있으며, 목적에 따라 출력값을 선택하여 활용할 수 있다. 제안된 모델의 일일 열 총수요 예측의 경우, 전체 MAPE(Mean Absolute Percentage Error, 평균 절대 비율 오차)가 개별 모델의 5.3~6.1%에서 5.2%로 향상되었고, 공휴일 열수요예측은 4.9~7.9%에서 2.9%로 크게 개선되었다. 본 연구에서는 한국 지역난방공사에서 제공한 특정 아파트 단지의 34개월 분량의(2015년 1월~ 2017년10월) 시간단위 열수요 데이터를 활용하였다.

• Journal of Korea Trade
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• 제25권4호
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• pp.17-36
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• 2021

Purpose - This study provides useful information to stakeholders by forecasting the tramp shipping market, which is a completely competitive market and has a huge fluctuation in freight rates due to low barriers to entry. Moreover, this study provides the most effective parameters for Baltic Dry Index (BDI) prediction and an optimal model by analyzing and comparing deep learning models such as the artificial neural network (ANN), recurrent neural network (RNN), and long short-term memory (LSTM). Design/methodology - This study uses various data models based on big data. The deep learning models considered are specialized for time series models. This study includes three perspectives to verify useful models in time series data by comparing prediction accuracy according to the selection of external variables and comparison between models. Findings - The BDI research reflecting the latest trends since 2015, using weekly data from 1995 to 2019 (25 years), is employed in this study. Additionally, we tried finding the best combination of BDI forecasts through the input of external factors such as supply, demand, raw materials, and economic aspects. Moreover, the combination of various unpredictable external variables and the fundamentals of supply and demand have sought to increase BDI prediction accuracy. Originality/value - Unlike previous studies, BDI forecasts reflect the latest stabilizing trends since 2015. Additionally, we look at the variation of the model's predictive accuracy according to the input of statistically validated variables. Moreover, we want to find the optimal model that minimizes the error value according to the parameter adjustment in the ANN model. Thus, this study helps future shipping stakeholders make decisions through BDI forecasts.

• 전기학회논문지
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• 제63권11호
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• pp.1503-1510
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• 2014

This study organizes scenarios on the power supply plans and electricity load forecasts considering their uncertainties and estimates natural gas quantity for electricity generation, total electricity supply cost and air pollutant emission of each scenario. Also the analysis is performed to check the properness of government's natural gas demand forecast and the possibility of achieving the government's CO2 emission target with the current plan and other scenarios. In result, no scenario satisfies the government's CO2 emission target and the natural gas demand could be doubled to the government's forecast. As under-forecast of natural gas demand has caused the increased natural gas procurement cost, it is required to consider uncertainties of power plant construction plan and electricity demand forecast in forecasting the natural gas demand. In addition, it is found that CO2 emission target could be achieved by enlarging natural gas use and demand-side management without big increase of total costs.

• 한국항공운항학회지
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• 제20권1호
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• pp.66-75
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• 2012

The purpose of this study is to analyze the effect of intervention variables which may affect the air travel demand for Jeju domestic flights and to anticipate the air travel demand for Jeju domestic flights. The air travel demand forecasts for Jeju domestic flights are conducted through ARIMA-Intervention Model selecting five intervention variables such as 2002 World Cup games, SARS, novel swine-origin influenza A, Yeonpyeongdo bombardment and Japan big earthquake. The result revealed that the risk factor such as the threat of war that is a negative intervention incident and occurred in Korea has the negative impact on the air travel demand due to the response of risk aversion by users. However, when local natural disasters (earthquakes, etc) occurring in neighboring courtiers and global outbreak of an epidemic gave the negligible impact to Korea, negative intervention incident would have a positive impact on air travel demand as a response to find alternative due to rational expectation of air travel customers. Also we realize that a mega-event such as the 2002 Korea-Japan World Cup games reduced the air travel demand in a short-term period unlike the perception in which it will increase the air travel demand and travel demands in the corresponding area.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2007년도 추계학술대회 및 정기총회
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• pp.183-189
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• 2007

In this paper, we developed the fuzzy group method data handling-type (GMDH) Model and applied it to demand forecasting of educational expenses. At present, GMDH family of modeling algorithms discovers the structure of empirical models and it gives only the way to get the most accurate identification and demand forecasts in case of noised and short input sampling. In distinction to fuzzy system, the results are explicit mathematical models, obtained in a relative short time. In this paper, an adaptive learning network is proposed as a kind of fuzzy GMDH. The proposed method can be reinterpreted as a multi-stage fuzzy decision rule which is called as the fuzzy GMDH. The fuzzy GMDH-type networks have several advantages compared with conventional multi-layered GMDH models. Therefore, many types of nonlinear systems can be automatically modeled by using the fuzzy GMDH. A computer program is developed and successful applications are shown in the field of demand forecasting problem of educational expenses with the number of factors considered.

• 산업경영시스템학회지
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• 제43권4호
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• pp.59-66
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• 2020

In this study, we consider the problem of forecasting the number of inbound foreigners visiting Korea. Forecasting tourism demand is an essential decision to plan related facilities and staffs, thus many studies have been carried out, mainly focusing on the number of inbound or outbound tourists. In order to forecast tourism demand, we use a seasonal ARIMA (SARIMA) model, as well as a SARIMAX model which additionally comprises an exogenous variable affecting the dependent variable, i.e., tourism demand. For constructing the forecasting model, we use a search procedure that can be used to determine the values of the orders of the SARIMA and SARIMAX. For the exogenous variable, we introduce factors that could cause the tourism demand reduction, such as the 9/11 attack, the SARS and MERS epidemic, and the deployment of THAAD. In this study, we propose a procedure, called Measuring Impact on Demand (MID), where the impact of each factor on tourism demand is measured and the value of the exogenous variable corresponding to the factor is determined based on the measurement. To show the performance of the proposed forecasting method, an empirical analysis was conducted where the monthly number of foreign visitors in 2019 were forecasted. It was shown that the proposed method can find more accurate forecasts than other benchmarks in terms of the mean absolute percentage error (MAPE).