• Journal of the Korean Data and Information Science Society
• /
• 제19권4호
• /
• pp.1345-1352
• /
• 2008

An inventory with constant demand is studied. We adopt a renewal argument to obtain the transient and stationary distribution of the level of the inventory. We show that the stationary distribution can be also derived by making use of either the level crossing technique or the renewal reward theorem. After assigning several managing costs to the inventory, we calculate the long-run average cost per unit time. A numerical example is illustrated to show how we optimize the inventory.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 1993년도 하계학술대회 논문집 A
• /
• pp.45-47
• /
• 1993

Load Management, is originated from efficiency improvement of energy use, or energy conservaion. Traditionally, electric utilities have constructed new power plants to meet the steadily increasing electricity demand. Power development planning, however, is becoming more difficult in the countries like Korea, Japan, and the United States, and increasing concerns about global environmental problems necessitate changes from existing supply-side options based on fossil-fuel to environmentally agreeable supply strategies. This paper discusses the demand side management strategy with emphasis on the concept, implementation scheme, and current practices employed in utilities.

• 대한설비공학회:학술대회논문집
• /
• 대한설비공학회 2006년도 하계학술발표대회 논문집
• /
• pp.264-269
• /
• 2006

Efficient load management on natural gas is strongly required to allow stable and reasonable energy use. The present study investigated domestic and international cases for demand management of natural gas. The directions of load management were discussed. The reasonable evaluation methods of demand management were analyzed and specific evaluation items were suggested.

• 한국정보시스템학회지:정보시스템연구
• /
• 제31권3호
• /
• pp.197-220
• /
• 2022

Purpose The purpose of this study is to identifies the demand pattern categorization of repair parts of Automotive After-sales Service(A/S) and proposes a demand prediction model for Auto repair parts using Long Short-Term Memory (LSTM) of artificial neural networks (ANN). The optimal parts inventory quantity prediction model is implemented by applying daily, weekly, and monthly the parts demand data to the LSTM model for the Lumpy demand which is irregularly in a specific period among repair parts of the Automotive A/S service. Design/methodology/approach This study classified the four demand pattern categorization with 2 years demand time-series data of repair parts according to the Average demand interval(ADI) and coefficient of variation (CV²) of demand size. Of the 16,295 parts in the A/S service shop studied, 96.5% had a Lumpy demand pattern that large quantities occurred at a specific period. lumpy demand pattern's repair parts in the last three years is predicted by applying them to the LSTM for daily, weekly, and monthly time-series data. as the model prediction performance evaluation index, MAPE, RMSE, and RMSLE that can measure the error between the predicted value and the actual value were used. Findings As a result of this study, Daily time-series data were excellently predicted as indicators with the lowest MAPE, RMSE, and RMSLE values, followed by Weekly and Monthly time-series data. This is due to the decrease in training data for Weekly and Monthly. even if the demand period is extended to get the training data, the prediction performance is still low due to the discontinuation of current vehicle models and the use of alternative parts that they are contributed to no more demand. Therefore, sufficient training data is important, but the selection of the prediction demand period is also a critical factor.

• The Journal of Asian Finance, Economics and Business
• /
• 제6권3호
• /
• pp.163-172
• /
• 2019

The paper aims to investigate the impact of the gap between domestic and international gold price on money demand in Vietnam, an emerging economy in the Asian region. We use a quarterly database collected from the first quarter of 2004 to the fourth quarter of 2016. The time-series database includes 52 observations. The money demand is represented by M2; Domestic income is the Gross domestic product at the constant prices of 1994; Inflation rate is calculated by the Customer Price Index from the General Statistics Office of Vietnam. The result confirms the existence of a long-term cointegration relationship between the money demand and the gap between domestic and international gold price as well as some variables including domestic income, inflation, and real exchange rate. The regression results also show that the gap between domestic and international gold price has a positive impact on money demand in the Vietnamese economy. Besides, the domestic income and international gold price have positive impacts on money demand while the inflation and real exchange rate are negatively related in the long run. This proves that the gap between the domestic and international gold price really has a positive impact on money demand in Vietnam during the study period.

• 산경연구논집
• /
• 제9권3호
• /
• pp.19-29
• /
• 2018

Purpose - This study empirically analyses the changes in unemployment rates to understand push factors of generating wage pressure and how it affects the aggregate demand in Korea and the United States. We use a structural macroeconomic model which is centered on the labor market and simultaneously explains the natural rate of unemployment and deviations. Research design, data and methodology - We attempt to empirically analyse the unemployment rates through two countries to analyse the economic effects of real wages and aggregate demand between 2000 and 2016. We introduce having estimated the whole model that the growth of unemployment into the part caused by each of these factors. Results - The results of this study show that in the long run, there is not only a natural level of employment but also a natural level of real demand are positively related. in the short run, demand can vary from bring about changes in employment by means of price or wage surprises. Conclusions - The pressure of demand in the labor market shows up strongly in both countries. The estimated labor-demand equation are consistent with this framework and generally have well defined real wage and demand effects.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2002년도 하계학술대회 논문집 A
• /
• pp.530-532
• /
• 2002

The contracted electric power and the demand factor of customers are used to predict the peak load in distribution transformers. The conventional demand factor was determined more than ten years ago. The contracted electric power and power demand have been increased. Therefore, we need to prepare the novel demand factor that appropriates at present. In this paper, we modify the demand factor to improve the peak load prediction of distribution transformers. To modify the demand factor, we utilize the 169 data acquisition devices for sample distribution transformers. The peak load currents were measured by the case studies using the actual load data, through which we verified that the proposed demand factors were correct than the conventional factors. A newly demand factor will be used to predict the peak load of distribution transformers.

• 전기학회논문지
• /
• 제60권7호
• /
• pp.1339-1346
• /
• 2011

This paper is developed to demand management scenario of energy consumption efficiency improvement, electricity generation efficiency improvement, network efficiency improvement, change of distribution ratio, movement of energy source, change of heating system, put of CHP to quantitatively assess to impact on energy use of demand management at the national level. This scenario can be applied Energy System Management model was developed based on Energy Balance Flow. In addition, effect analysis through built demand management scenario was quantitatively evaluated integrated demand management effectiveness of energy cost saving, CO2 emission reduction and energy savings of national level by calculating to primary energy source usage change in terms of integration demand management effect more often than not a single energy source separated electricity, heat and gas.

• 한국인공지능학회지
• /
• 제10권1호
• /
• pp.27-32
• /
• 2022

As the need for eco-friendly transportation increases due to the deepening climate crisis, many local governments in Korea are introducing shared bicycles. Due to anxiety about public transportation after COVID-19, bicycles have firmly established themselves as the axis of daily transportation. The use of shared bicycles is spread, and the demand for bicycles is increasing by rental offices, but there are operational and management difficulties because the demand is managed under a limited budget. And unfortunately, user behavior results in a spatial imbalance of the bike inventory over time. So, in order to easily operate the maintenance of shared bicycles in Seoul, bicycles should be prepared in large quantities at a time of high demand and withdrawn at a low time. Therefore, in this study, by using machine learning, the linear regression algorithm and MS Azure ML are used to predict and analyze when demand is high. As a result of the analysis, the demand for bicycles in 2018 is on the rise compared to 2017, and the demand is lower in winter than in spring, summer, and fall. It can be judged that this linear regression-based prediction can reduce maintenance and management costs in a shared society and increase user convenience. In a further study, we will focus on shared bike routes by using GPS tracking systems. Through the data found, the route used by most people will be analyzed to derive the optimal route when installing a bicycle-only road.

• 한국주거학회논문집
• /
• 제23권6호
• /
• pp.31-38
• /
• 2012

In this paper, we draws tendency of the electricity consumption in residential buildings according to inhabitants Composition types and the level of incomes. it is necessary to reduce energy cost and keep energy security through the electricity demand forecasting and management technology. Progressive social change such as increases of single household, the aging of society, increases in the income level will replace the existing residential electricity demand pattern. However, Only with conventional methods that using only the energy consumption per-unit area are based on Energy final consumption data can not respond to those social and environmental change. To develop electricity demand estimation model that can cope flexibly to changes in the social and environmental, In this paper researches propensity of electricity consumption according to the type of residents configuration, the level of income. First, we typed form of inhabitants in residential that existed in Korea. after that we calculated hourly electricity consumption for each type through National Time-Use Survey performed at the National Statistical Office with considering overlapping behavior. Household appliances and retention standards according to income level is also considered.