• Journal of Korean Society of Transportation
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• v.26 no.6
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• pp.7-15
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• 2008

Due to the limitations in the statistical data, the existing studies adopted rough methodologies with strong assumptions in the estimation of international passenger travel demand forecast in the ASEAN region. This study aims to develop international passenger travel demand models using scientific methodologies. This study proposes a direct demand model using the immigration and emigration data between countries in the region. This is because of the difficulty of estimating trip generation and trip distribution separately due to the data limitation in the region. As there does not exist the mode choice model for the region, this study estimates a mode choice model using the Stated Preference technique. The mode choice model is separated into three categories of models according to the range of distance between the origin and destination of travel; this is to reflect the different behavior in mode choice according to the travel distance. The result of model estimations suggests that the estimated models produce resonable results statistically. It is expected that the proposed models are useful for the future travel demand estimation in the ASEAN region.

• The Korean Journal of Applied Statistics
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• v.24 no.1
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• pp.93-102
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• 2011

In this paper, we compared the performance of the several time series models for tourism demand forecasting. We showed that seasonal effects in the data(Japan, China, USA, and Philippines) exist in the tourism data and the forecasting accuracies are compared by the RMSE criterion.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.567-570
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• 2016

전력수요 예측은 설비투자, 수급 안정, 구매전력비 등에 직결되는 중요한 요소이며 국가 경제에 미치는 영향이 크다. 특히 인구가 밀집한 대도시의 경우 정치, 교육, 문화, 경제적 활동들이 전력사용과 밀접한 연관이 있어 안정적인 전력공급을 위한 정확한 전력수요 예측이 필요하다. 최근 평균기온 및 국내총생산을 독립변수로 활용하여 다중회귀모형을 구성한 연구가 전국 단위 전력수요 예측에 유용한 결과를 보여주었다. 하지만 좀 더 작은 단위 지역의 전력수요를 예측할 때에는 지역마다 제반 여건에 따른 전력사용 용도가 다르므로, 그 지역의 전력수요와 상관관계가 높은 다른 변수들을 함께 고려해야 할 필요가 있다. 본 논문은 서울시 자치구별 월 단위 전력수요 예측을 위하여 과거 전력수요량을 독립변수, 평균기온, 지역내총생산, 자치구별 인구, 세대수, 지하철 승 하차 인원을 종속변수로 설정한 다중회귀모형을 구성하였다. 이를 기반으로 다양한 실험을 통해 자치구별 월간 전력수요 예측을 진행하였으며, 그 결과 이전보다 향상된 정확도를 얻을 수 있었다.

• Proceedings of the Korea Water Resources Association Conference
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• 2004.05b
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• pp.1408-1412
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• 2004

인구의 폭발적 증가, 산업화, 도시화의 급진적, 과학기숙의 발달 등으로 물 소비는 급증하는 반면, 이상기후현상으로 수자원의 절대량이 줄어 수자원의 양적인 문제와 하천 및 저수지의 수질오염에 대한 질적인 문제가 ,대두되고 있다. 하천의 수질현상 및 이송은 상당히 비선형적이고, 시간에 따라 변화하려, 실제로 수질의 예측은 유량의 변동, 오염물질의 이송 및 확산, 하천 구조물 등의 여러 요인에 의하여 상당히 어렵다고 알려져 왔다. 또한 한정된 수자원으로 하천의 수량과 수질목표를 동시에 달성하기 위해서는 물의 수요와 공급을 실시간으로 감시하면서 기상과 유출예측기술을 활용하여 용수의 수요와 공급을 예측하고 이를 토대로 수량과 수질을 고려한 물관리 운영시스템이 구축되어야 한다. 이를 위해 본 연구에서는 모형의 입${\cdot}$출력 구성을 자유롭게 변형할 수 있는 상태공간 모형과 신경망 모형을 이용하여 금강수계 주요 지점의 수질예측 모형을 구성하고 모형의 적용성을 파악한 후 예측력이 우수한 모형을 Web기반 모형의 수질예측 모듈의 기본모형으로 선정하고 Web 상에서 수질예측이 가능하도록 시스템을 개발하였다.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.1
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• pp.131-136
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• 2014

In this paper, we develop a fashion products demand forecasting algorithm using ARIMA model and Wavelet Neural Networks model. To show effectiveness of the proposed method, we analyzed characteristics of time-series data collected in "H" company during 2008-2012 and then performed the proposed method through various analyses. As noted in experimental results, the performance of three types model such as ARIMA, Wavelet Neural Networks and ARIMA + Wavelet Neural Networks show 5.179%, 4.553%, and 4.448.% with respect to MAPE(Mean Absolute Percentage Error), respectively. Thus, it is noted that the proposed method can be used to predict fashion products demand for efficient of operation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.4
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• pp.261-273
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• 2022

This study aims to predict the land use demand of Seongnam-city using system dynamics and to simulate the effect of changes in family structure and land use density adjustment policy on land use demand. This study attempted to construct causal loop diagrams and an analysis model. The changes in land use demand over time were predicted through simulation results. As a result of the analysis, as of 2035, an additional supply of 2.08 km² for residential land and 1.36 km² for commercial land is required. Additionally, the current supply area of industrial land can meet the demand. Three policy experiments were conducted by changing the variable values in the basic model. In the first policy experiment, it was found that when the number of household members decreased sharply compared to the basic model, up to 7.99 km² of additional residential land were required. In the second policy experiment, if the apartment floor area ratio was raised from 200% to 300%, it was possible to meet the demand for residential land with the current supply area of Seongnam-city. In the third policy experiment, it was found that even if the average number of floors in the commercial area was raised from four to five and the building-to-land ratio in the commercial area was raised from 80% to 85%, the demand for commercial land exceeded the supply area of the commercial area in Seongnam-city. This study is meaningful in that it proposes a new analytical model for land use demand prediction using system dynamics, and empirically analyzes the model by applying the actual urban planning status and statistics of Seongnam-city.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.42-42
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• 2016

지난 135년(1880~2014년) 동안 지구온난화에 따른 기후변화로 지구의 평균 기온은 $0.85^{\circ}C$ 상승하였으며, 이는 수문현상에 영향을 미쳐 강우량 및 강우강도가 증가하는 경향성을 보이고 있다. 이처럼 기후변화로 말미암아 수문 현상의 변화에 따른 불확실성이 커져 물 순환 과정의 정확한 파악이 더욱 어려워지고 있다. 따라서 미래 안정적인 물 공급을 위한 수자원계획 수립 및 관리를 위해 기후변화를 고려한 물 수요 예측이 필요하다고 하겠다. 본 연구에서는 도시화에 따라 물수요가 변화하고 있는 안성천을 대상유역으로 선정하여 기후변화를 고려한 미래 물 부족량을 산정하고자 하였다. 이를 위해, 기후변화 RCP 8.5 시나리오를 이용하여 미래 강수량을 모의하였고, 준 분포 강우-유출 모형인 SLURP 모형을 이용하여 미래 유출량을 분석하였다. 미래 유출량을 토대로 정확한 물 수요 예측을 위해 통합수자원평가계획 모형인 K-WEAP 모형을 이용하여 소유역별 물수지 분석을 위한 네트워크 및 시나리오를 구성하였다. 또한 용수이용량(생활, 공업, 농업용수)의 과거자료를 활용한 선형예측함수식을 통해 장래 물 수요 추정량을 산정하였다. 물 수지 분석 결과, 안성천 유역은 인구 증가, 급격한 도시화로 인해 용수 이용량이 증가하고 있었으나 농업용수는 점차적으로 감소하고 있었다. 따라서 생활 및 공업 용수에 대한 수요를 충족하지 못해 미래 물 부족량이 증가하고 있는 것으로 확인되었으며, 본 연구에서는 물부족 해소를 위한 방안으로 광역상수도 확충과 제한급수를 제시하여 분석을 수행하였다.

• Journal of Korean Society of Forest Science
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• v.98 no.3
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• pp.284-289
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• 2009

This study estimated the fiberboard demand using VAR and econometric model, and compared the prediction accuracy of the two models. And the variance decomposition and impulse response were analyzed using VAR model, and predicted the fiberboard demand. The VAR model was specified with lagged dependent variable, lagged own price, lagged construction product, dummy. The econometric model was specified with own price, substitute price, construction product, dummy. The dummy variable reflected the abrupt decrease in fiberboard demand in the late 1990's. The results showed that the fiberboard demand prediction can be performed more accurately by VAR model than by econometric model. In the VAR model of fiberboard demand, after twelve months, the construction product change accounts for about fifty percent of variation in the demand, and the own price change accounts for about thirty percent of variation in the demand. On the other hand, the impact of a shock to the construction product is significant for about twelve months on the demand of fiberboard, and the impact of a shock to the own price is significant for about six months on the demand of fiberboard.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.3
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• pp.1488-1494
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• 2011

In this paper, we propose an algorithm using linear regression model that forecasts the demand of heated water in winter. To supply heated water to apartments, stores and office buildings, Korea District Heating Corp.(KDHC) operates boilers including electric power generators. In order to operate facilities generating heated water economically, it is essential to forecast daily demand of heated water with accuracy. Analysis of history data of Kangnam Branch of KDHC in 2006 and 2007 reveals that heated water supply on previous day as well as temperature are the most important factors to forecast the daily demand of heated water. When calculated by the proposed regression model, mean absolute percentage error for the demand of heated water in winter of the year 2006 through 2009 does not exceed 3.87%.

• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.275-284
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• 2016

Accurate electricity demand forecasts is essential in reducing energy spend and preventing imbalance of the power supply. In forcasting electricity demand, we considered double seasonal Holt-Winters model and TBATS model with sliding window. We selected a specific time zone as the reference line of daily electric demand because it is least likely to be influenced by external factors. The forecasting performance have been evaluated in terms of RMSE and MAPE criteria. We used the observations ranging January 4, 2009 to December 31 for testing data. For validation data, the records has been used between January 1, 2012 and December 29, 2012.