• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.1218-1221
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• 2022

최근 전세계의 탄소중립 요구에 따른 에너지 효율 증대를 통한 에너지 절감을 위한 효율성 관련 연구가 확대되고 있다. 방송과 미디어 분야에는 에너지 효율이 더욱 시급하다. 이에 본 연구에서는 효율적인 에너지 시스템 구축을 위해 난방 에너지 시계열 데이터를 기반으로 한 수요 예측 모델을 선정하고, 설명하는 인공지능 모델을 도입하여 수요 예측에 영향을 미치는 원인을 파악하는 프레임워크를 제안한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.11a
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• pp.824-826
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• 2017

수요예측은 적정 재고를 유지하기 위해 선행되어야 할 중요한 부분이라 할 수 있다. 수요예측의 정확도 향상이 적정한 재고를 유지하기 위한 토대가 된다. 하지만 수요예측을 어렵게 만드는 주요 원인 중 하나인 간헐적인 수요는 기존 시계열 기법으로 예측하는데 있어 어려움이 크다. 본 연구에서는 인공지능의 한 기법인 인공신경망을 적용하여 간헐적 품목에 대한 수요예측을 실시하였다. 6개의 기법을 통해 실험을 실시한 결과 인공신경망이 가장 오차가 적은 우수한 결과를 나타냈다.

• Environmental and Resource Economics Review
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• v.21 no.2
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• pp.341-369
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• 2012

Population aging has been one of the serious problems in Korea. Aging can affect social and economic features including energy consumption. This paper analyzed how population aging makes an effect on residential electricity demand. Yearly data from 1965 to 2010 were collected. The long and short-run demands for residential electricity were estimated with respect to Korean aging index. The results show that population aging reduces residential electricity demands in the short run significantly, but the effect decreases in the long run. However, population aging still negatively affects residential electricity consumption in long run. If population keep aging as Korean government expected, then the residential electricity demand per capita will grow less than 3%.

• The Journal of Bigdata
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• v.9 no.1
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• pp.31-47
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• 2024

With the advancement of artificial intelligence, the travel and hospitality industry is also adopting AI and machine learning technologies for various purposes. In the tourism industry, demand forecasting is recognized as a very important factor, as it directly impacts service efficiency and revenue maximization. Demand forecasting requires the consideration of time-varying data flows, which is why statistical techniques and machine learning models are used. In recent years, variations and integration of existing models have been studied to account for the diversity of demand forecasting data and the complexity of the natural world, which have been reported to improve forecasting performance concerning uncertainty and variability. This study also proposes a new model that integrates various machine-learning approaches to improve the accuracy of hotel sales demand forecasting. Specifically, this study proposes a new time series forecasting model based on XGBoost that selectively utilizes a local model by clustering with DTW K-means and a global model using the entire data to improve forecasting performance. The hotel demand forecasting model that selectively utilizes global and regional models proposed in this study is expected to impact the growth of the hotel and travel industry positively and can be applied to forecasting in other business fields in the future.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1601-1607
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• 2016

Recently, weather information has been increasingly used in various area. This study presents the necessity of hourly weather information for electricity demand forecasting through correlation analysis and multivariate regression model. Hourly weather data were collected by Meteorological Administration. Using electricity demand data, we considered TBATS exponential smoothing model with a sliding window method in order to forecast electricity demand. In this paper, we have shown that the incorporation of weather infromation into electrocity demand models can significantly enhance a forecasting capability.

• The Korean Journal of Applied Statistics
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• v.32 no.1
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• pp.139-148
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• 2019

This study proposed a multiple intervention time series model to predict KTX passenger demand. In order to revise the research of Kim and Kim (Korean Society for Railway, 14, 470-476, 2011) considering only the intervention of the second phase of Gyeong-bu before November of 2011, we adopted multiple intervention seasonal ARIMA models to model the time series data with additional interventions which occurred after November of 2011. Through the data analysis, it was confirmed that the effects of various interventions such as Gyeong-bu and Ho-nam 2 phase, outbreak of MERS and national holidays, which affected the KTX transportation demand, are successfully explained and the prediction accuracy could be quite improved significantly.

• Journal of Korean Society of Transportation
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• v.35 no.2
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• pp.143-159
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• 2017

For forecasting air cargo demand from Incheon National Airport to all of airports in the United States (US), this study employed the Seasonal Autoregressive Integrated Moving Average (SARIMA) method and the time-series data collected from the first quarter of 2003 to the second quarter of 2016. By comparing the SARIMA method against the ARIMA method, it was found that the SARIMA method performs well, relatively with time series data highlighting seasonal periodic characteristics. While existing previous research was generally focused on the air passenger and the air cargo as a whole rather than specific air routes, this study emphasized on a specific air cargo demand to the US route. The meaningful findings would support the future research.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 2008.04a
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• pp.193-198
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• 2008

• Journal of Korean Society of Transportation
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• v.26 no.5
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• pp.81-90
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• 2008

Bus passengers need some time to adapt to the changed bus route or free bus transfer system which is part of the public transportation system restructuring plan. This research is focused on the characteristics of monthly patterns of bus passengers. The period of stabilization of bus passenger demand after the rearrangement of bus route system by a time series were analysed. In order to look into the characteristics of bus passenger demand by month, data on the number of monthly bus passengers of recent five years in metropolitan cities across the nation was collected. Kendall's coefficient of concordance is used to test whether the cities showed concordance with respect to the number of monthly bus passengers during a period of five years. The study collected and performed a time series analysis of data on the number of monthly bus passengers during the past ten years in Daegu metropolitan area which carried out a new bus route plan in February 2006. The number of monthly bus passengers in 2006 was estimated using the time series analysis. The city of Daegu found that after six months the estimated and actual values displayed a similar pattern. This result can be applied to other cities in estimating the passenger demands in the future.

• The Journal of the Korea Contents Association
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• v.15 no.6
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• pp.399-410
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• 2015

The primary goal of the study is to examine the possibility of applying the time series model to forecasting demand and supply of social services. In the study, we used survey data based on a nationally represented sample which is secondary processed data. We selected developmental rehabilitation service. The analysis, we made models of a demand and a supply using time series analysis. Utilizing the estimates, we identified each model's pattern. This study provides an empirical evidence to suggest benefits of using the time series model for forecasting the demand and the supply pattern of newly introduced social services. We also provide discussions on policy implications of utilizing demand and supply time series models in the process of developing new social services.