• Journal of the Korean Society for Aviation and Aeronautics
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• v.27 no.3
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• pp.98-105
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• 2019

In the past 30 years, our aviation demand has been growing continuously. As such, the importance of the demand forecasting field is increasing. In this study, the factors influencing Korea's international air demand were selected, and the international air demand was analyzed, forecasted and reviewed through OLS multiple regression analysis. As a result, passenger demand was affected by GDP per capita, oil price and exchange rate, while cargo demand was affected by GDP per capita and private consumption growth rate. In particular, passenger demand was analyzed to be sensitive to temporary external shocks, and cargo demand was more affected by economic variables than temporary external shocks. Demand forecasting, OLS multiple regression analysis, passenger demand, cargo demand, transient external shocks, economic variables.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.27 no.1
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• pp.43-50
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• 2019

The aviation demand forecast field has been actively studied along with the recent growth of the aviation market. In this study, the demand for domestic passenger demand and freight demand was estimated through cross-validation method. As a result, passenger demand is influenced by private consumption growth rate, oil price, and exchange rate. Freight demand is affected by GDP per capita, private consumption growth rate, and oil price. In particular, passenger demand is characterized by temporary external shocks, and freight demand is more affected by economic variables than temporary shocks.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.3
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• pp.97-104
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• 2017

Reinforcement of insulation in apartment buildings reduces the heating and cooling energy consumption by lowering the heat transfer in the building envelope. There are differences between internal and external insulation methods in heat transmission properties. However, some building load calculation programs cannot analysis the differences between the two. This is because these programs do no account for the timelag or thermal storage effect of the wall according to the location of insulation. In this study, the heat transmission characteristics of internal and external insulation were analyzed by EnergyPlus, and heating and cooling energy demand was compared. The results showed that external insulation system had lower heating and cooling loads than internal insulation system. Also the heat transfer rate of external insulation is steadier than internal insulation. About 13.6% of heating and cooling energy demand decreased when the outdoor wall was finished with external insulation compared to the demand with internal insulation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.35 no.4
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• pp.907-920
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• 2015

By increase owning vehicle, infrastructure that accept vehicle is very poor on present that People's commuting is rapidly change to vehicle-use-form in metropolitan area. Although Transportation demand management is enforced, traffic is heavy but studies lake in internal and external. This study select Transportation demand management that enforce in internal and external and do a survey. Based on this survey, conduct AHP (Analytic Hierarchy Process) analvsis, Transportation demand management that enforce internal and external compare, decide superiority and understand every particular items' importance and satisfaction that users think. Also based on importance that collect by AHP analysis compare Transportation demand management character. Finally figure that grasped by this study, analysis present, found future TDM course and applicate future transportation improvement.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.528-550
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• 2024

Warehousing demand prediction is an essential part of the supply chain, providing a fundamental basis for product manufacturing, replenishment, warehouse planning, etc. Existing forecasting methods cannot produce accurate forecasts since warehouse demand is affected by external factors such as holidays and seasons. Some aspects, such as consumer psychology and producer reputation, are challenging to quantify. The data can fluctuate widely or do not show obvious trend cycles. We introduce a new model for warehouse demand prediction called MAGRU, which stands for Multi-layer Attention with GRU. In the model, firstly, we perform the embedding operation on the input sequence to quantify the external influences; after that, we implement an encoder using GRU and the attention mechanism. The hidden state of GRU captures essential time series. In the decoder, we use attention again to select the key hidden states among all-time slices as the data to be fed into the GRU network. Experimental results show that this model has higher accuracy than RNN, LSTM, GRU, Prophet, XGboost, and DARNN. Using mean absolute error (MAE) and symmetric mean absolute percentage error(SMAPE) to evaluate the experimental results, MAGRU's MAE, RMSE, and SMAPE decreased by 7.65%, 10.03%, and 8.87% over GRU-LSTM, the current best model for solving this type of problem.

• KIEAE Journal
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• v.15 no.4
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• pp.77-84
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• 2015

Purpose: In 2008, As the green growth policy was presented, Green Building is made any effort to propagation. In this paper, the respective technologies that are able to considerably reduce the energy demands for heating, cooling, hot-water, lighting and ventilation among the variety of technologies were selected. Method: Design factors such as (1) External insulation, (2) Triple glazing window, (3) LED lighting, (4) External venetian blind, (5) Geothermal and (6) Heat recovery ventilator were derived. In addition, energy saving effects in terms of energy demand, energy consumption and energy cost were investigated using EnergyPlus, building energy analysis tool. Result : The results were as follows. (1) It can be seen that high insulated triple glazing window, heat recovery ventilator and external insulation technology is excellent for energy demand. (2) Unlike energy demand, saving effect of energy consumption and energy cost was shown in order of Geothermal > Triple Window > Heat recovery Ventilation> Insulation> LED Lighting > EVB Blind.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.395-406
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• 2017

The purpose of this study is to identify the pattern of daily electricity demand through clustering and classification. The hourly data was collected by KPS (Korea Power Exchange) between 2008 and 2012. The time trend was eliminated for conducting the pattern of daily electricity demand because electricity demand data is times series data. We have considered k-means clustering, Gaussian mixture model clustering, and functional clustering in order to find the optimal clustering method. The classification analysis was conducted to understand the relationship between external factors, day of the week, holiday, and weather. Data was divided into training data and test data. Training data consisted of external factors and clustered number between 2008 and 2011. Test data was daily data of external factors in 2012. Decision tree, random forest, Support vector machine, and Naive Bayes were used. As a result, Gaussian model based clustering and random forest showed the best prediction performance when the number of cluster was 8.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.35-37
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• 2021

Analyzing and predicting foreign tourists' demand is a crucial research topic in the tourism industry because it profoundly influences establishing and planning tourism policies. Since foreign tourist data is influenced by various external factors, it has a characteristic that there are many subtle changes over time. Therefore, in recent years, research is being conducted to design a prediction model by reflecting various external factors such as economic variables to predict the demand for tourists inbound. However, the regression analysis model and the recurrent neural network model, mainly used for time series prediction, did not show good performance in time series prediction reflecting various variables. Therefore, we design a foreign tourist demand prediction model that complements these limitations using a convolutional neural network. In this paper, we propose a model that predicts foreign tourists' demand by designing a one-dimensional convolutional neural network that reflects foreign tourist data for the past ten years provided by the Korea Tourism Organization and additionally collected external factors as input variables.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.8
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• pp.1342-1348
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• 2008

One important objective of the electricity market is to decrease the price by ensuring stability in the market operation. Interconnected to this is another objective; namely, to realize sustainable consumption of electricity by equitably distributing the effects and benefits of participating in the market among all participants of the industry. One method that can help achieve these objectives is the ^{(R)}$demand-response program, - which allows for active adjustment of the loadage from the demand side in response to the price. The demand-response program requires a customer baseline load (CBL), a criterion of calculating the success of decreases in demand. This study was conducted in order to calculate undistorted CBL by analyzing the correlations between such external or seasonal factors as temperature, humidity, and discomfort indices and the amounts of electricity consumed. The method and findings of this study are accordingly explicated.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.575-583
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• 2020

Vietnam is the country with the largest animal feed production in Southeast Asia. Domestic animal feed manufacturing enterprises play an important role in animal husbandry in particular and in agriculture in general. However, domestic animal feed enterprises in Vietnam are encountering shortcomings. This paper is conducted to investigate the impact levels of external determinants on business performance of domestic animal feed manufacturing enterprises, including: (i) policy and economic mechanism, (ii) supply-demand of animal feed products, and (iii) nature and level of market competition. We presented a research method, explaining the dependent variable 'business performance' and the independent variables. Data were collected from 120 questionnaires from domestic animal feed manufacturing enterprises. Based on these data, we use Cronbach's Alpha, EFA and run regression model for assessing the impact levels of each independent variable on the dependent variable of business performance of domestic animal feed manufacturing enterprises. The results show that three external determinants including (i) policy and economic mechanism, (ii) supply-demand of animal feed products, and (iii) nature and level of market competition, have positive relationships with business performance. Based on the findings, some recommendations are given for improving business performance of domestic animal feed manufacturing enterprises to ensure sustainability.