• Electronics and Telecommunications Trends
• /
• v.17 no.4 s.76
• /
• pp.75-80
• /
• 2002

본 고에서 저자는 신규 통신 서비스로서 공중 무선 LAN 서비스의 수요 예측에 대해 다룬다. 신규 사업에 있어서 수요 예측은 사업의 수익성을 평가하는 가장 기본적인 자료이며 효과적인 마케팅 전략 수립을 위한 기초 단계로서 의미가 크다. 그러나 신규 서비스는 특성상 과거의 판매 자료가 존재하지 않기 때문에 시계열 자료를 이용한 수요 예측이 불가능하다. 따라서 본 고에서는 공중 무선 LAN 서비스와 유사한 특성을 지닐 것으로 판명되는 기존 서비스인 ADSL/케이블모뎀 서비스와 이동전화 서비스의 과거의 확산 과정을 분석하여 공중 무선 LAN 서비스의 확산 과정을 살펴본다. 이러한 유사추론과정을 통해 2006년까지 공중 무선 LAN 서비스의 가입자 수를 예측한다. 또한 선택모형(choice model)을 이용한 잠재 시장 규모의 추정법에 대해 언급한다.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.8
• /
• pp.19-26
• /
• 2014

Analysis of influential factors on water consumption in households will help predicting the water demand of end-use in household and give an explanation to cause on the change of trend. In this research, the data are gathered by radio telemetry system which is combined electronic flow-meter and wireless communication system in 140 household in Korea. Using this data, we estimate for each residential type to determine liter per capita day. we used real data to predict bathtub and washbowl water-uses and compared the ordinary least square regression model and autoregressive regression error model. The results of this study can be applied in the planning stages of water and waste water facilities.

• Proceedings of the KIEE Conference
• /
• 2011.07a
• /
• pp.616-617
• /
• 2011

최대전력수요를 분석함에 있어 발생 당시의 기온 실적이 반영된 실적 최대전력만을 사용함으로 다양한 통계적 착시현상이 나타나고 있다. 평균적인 기상 상태에서의 최대전력수요를 측정하기 어려워 신뢰성있는 예측수요를 도출하기에도 많은 한계가 발생한다. 따라서 역사적 기온데이터에 기반한 정상적인 기온분포를 "표준기온분포"로 새롭게 정의하고, 이를 반영한 최대전력수요를 "기온보정 최대전력 수요"로 규정함으로써, 기존의 통계적 착시현상을 배제하고, 정확도 높은 최대전력 수요 예측치를 도출하여, 안정적 전력수급에 큰 기여가 있을 것으로 기대한다. 또한 본 연구에서는 기온보정 최대전력을 도출하기 위해 공적분 및 오차수정이론을 반영하여 모형화하였고, 엄격한 통계적 방법론을 이용하여 관련 모형을 검증하였다.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.10 no.5
• /
• pp.113-123
• /
• 2011

Recently, the global warming problem has arised around world, many nations has set up a various regulations for decreasing $CO_2$. In particular, $CO_2$ emissions reduction effect is very powerful in transport part, so there is a rising interest about development of green car, or electric vehicle in auto industry. For this reason, it is important to make a strategy for charging infra and forcast electric power demand, but it hasn't introduced about demand forecasting electric vehicle. Thus, this paper presents a demand forecasting for electric vehicles using choice based multigeneration diffusion model. In this paper, it estimates innovation coefficient, immitation coefficient in Bass model by using hybrid car market data and forecast electric vehicle market by year using potential demand market through SP(Stated Preference) experiment. Also, It facilitates more accurate demand forecasting electric vehicle market refelcting multigeneration diffusion model in accordance with attribute progress in development of electric vehicle. Through demand forecasting methodology in this paper, it can be utilized power supply and building a charging infra in the future.

• The Korean Journal of Applied Statistics
• /
• v.32 no.1
• /
• pp.111-128
• /
• 2019

Subways are eco-friendly public transportation that can transport large numbers of passengers safely and quickly. It is necessary to predict the accurate number of passengers in order to increase public interest in subway. This study groups stations on Lines 1 to 9 of the Seoul Metropolitan Subway using clustering analysis. We propose one final prediction model for all stations and three optimal prediction models for each cluster. We found three groups of stations out of 294 total subway stations. The Group 1 area is industrial and commercial, the Group 2 ares is residential and commercial, and the Group 3 area is residential districts. Various data mining techniques were conducted for each group, as well as driving some influential factors on demand prediction. We use our model to predict the number of passengers for 8 new stations which are part of the 3rd extension plan of Seoul metro line 9 opened in October 2018. The estimated average number of passengers per hour is from 241 to 452 and the estimated maximum number of passengers per hour is from 969 to 1515. We believe our analysis can help improve the efficiency of public transportation policy.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.16 no.10
• /
• pp.6860-6868
• /
• 2015

In this study, we consider a problem of forecasting daily city gas demand of Korea. Forecasting daily gas demand is a daily routine for gas provider, and gas demand needs to be forecasted accurately in order to guarantee secure gas supply. In this study, we analyze the time series of city gas demand in several ways. Data analysis shows that primary factors affecting the city gas demand include the demand of previous day, temperature, day of week, and so on. Incorporating these factors, we developed a multiple linear regression model. Also, we devised a sampling procedure that selectively collects the past data considering the characteristics of the city gas demand. Test results on real data exhibit that the MAPE (Mean Absolute Percentage Error) obtained by the proposed method is about 2.22%, which amounts to 7% of the relative improvement ratio when compared with the existing method in the literature.

• 월간 기계설비
• /
• s.35
• /
• pp.56-69
• /
• 1993

건설산업이 점차 노동집약적 산업에서 기술집약적 산업으로 전환됨에 따라 요구되는 건설기술인력의 수요가 증가할 것으로 예상된다. 이에 따라 건설기술인력의 효율적인 수급을 위해 기술 인력의 공급량과 수요량을 예측하여 예견되는 불균형 또는 균형적인 소요량을 제시함으써 사회의 투자의사결정에 수반되는 불확실성의 감소, 경제계획수립의 기초자료, 정부의 정책이나 프로그램의 효과에 대한 평가, 인력 확보를 위한 교육$\cdot$훈련체계 수립, 그리고 기업의 합리적 의사결정을 위한 자료로 활용할 수 있는 기준을 제공할것으로 기대된다. 따라서 본 연구에서는 건설기술인력이 수요동향 분석을 위하여 설문조사와 건설업통계조사보고서를 이용하여 건설업에 종사하는 건설업 취업자 추이, 고용구조, 건설기술의 현황을 분석하여 건설업의 종사자 및 기술인력에 대한 추이를 규명하였다. 이러한 분석을 토대로 경제요인을 고려한 건설기술인력의 수요예측모형을 제시하고 이 모형을 사용하여 2000년까지의 분야별 건설기술인력의 수요를 예측하였다.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.28 no.6D
• /
• pp.791-799
• /
• 2008

To build the system which has high utilization and usefulness for users, it is necessary to know the information type and use-demand that the use want. The purpose of this study is to forecast the preference and demand of utilization for bus information when bus information is offered through cellular phon. The accomplishments of this research are as follow : Firstly, importance on the level of individual factor and the value of change's figure can be evaluated, using preference analysis on bus information by conjoint analysis. Secondly, by establishing the use-demand model bus information using binary logit model, influence factor on whether or not the use of the user. Finally, ordered probit model was built by use behavior model in payment per call or per month of potential user of bus information. Through call times and sensitive analysis by payment methods, elasticity point, optimal payment fee, and use probability was analyzed. This study make application as basic to efficient bus information policy and to improve use rate of bus information in future because this study make it possible to get preference analysis, use-demand analysis and estimation of optimal payment fee which is reflecting various requirement in use of bus information user.

• The Journal of Bigdata
• /
• v.3 no.2
• /
• pp.59-70
• /
• 2018

In order to provide stable district heat supplying service to the certain limited residential area, it is the most important to forecast the short-term future demand more accurately and produce and supply heat in efficient way. However, it is very difficult to develop a universal heat demand forecasting model that can be applied to general situations because the factors affecting the heat consumption are very diverse and the consumption patterns are changed according to individual consumers and regional characteristics. In particular, considering all of the various variables that can affect heat demand does not help improve performance in terms of accuracy and versatility. Therefore, this study aims to develop a demand forecasting model using deep learning based on only limited information that can be acquired in real time. A demand forecasting model was developed by learning the artificial neural network of the Tensorflow using past data consisting only of the outdoor temperature of the area and date as input variables. The performance of the proposed model was evaluated by comparing the accuracy of demand predicted with the previous regression model. The proposed heat demand forecasting model in this research showed that it is possible to enhance the accuracy using only limited variables which can be secured in real time. For the demand forecasting in a certain region, the proposed model can be customized by adding some features which can reflect the regional characteristics.

• Journal of Korean Society of Transportation
• /
• v.26 no.5
• /
• pp.163-174
• /
• 2008

The conventional four-step travel demand model is still widely used as the state-of-practice in most transportation planning agencies even though it does not provide reliable estimates of travel demand. In order to improve the accuracy of travel demand estimation, implementing an alternative approach would be critical as much as acquiring reliable socioeconomic and travel data. Recently, the role of travel demand model is diverse to satisfy the needs of microscopic analysis regarding various policies of travel demand management and traffic operations. In this context, the activity-based approach for travel demand estimation is introduced and a case study of developing a spatial-temporal activity presence-based approach that estimates travel demand through forecasting number of people present at certain place and time is accomplished. Results show that the spatial-temporal activity presence-based approach provides reliable estimates of both number of people present and trips actually people made. It is expected that the proposed approach will provide better estimates and be used in not only long-term transport plans but short-term transport impact studies with respect to various transport policies. Finally, in order to introduce the spatial-temporal activity presence-based approach, the data such as activity-based travel diary and land use based on geographic information system are essential.