• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.9
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• pp.417-424
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• 2002

This paper presents an industrial peak load management system for the peak demand control. Kohonen neural network and wavelet transform based techniques are adopted for industrial peak load forecasting that will be used as input data of the peak demand control. Firstly, one year of historical load data of a steel company were sorted and clustered into several groups using Kohonen neural network and then wavelet transforms are applied with Biorthogonal 1.3 mother wavelet in order to forecast the peak load of one minute ahead. In addition, for the peak demand control, composite fuzzy model is proposed and implemented in this work. The results are compared with those of conventional model, fuzzy model and composite model, respectively. The outcome of the study clearly indicates that the composite fuzzy model approach can be used as an attractive and effective means of the peak demand control.

• 전기의세계
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• v.29 no.4
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• pp.260-264
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• 1980

In this paper, a new algorithm is derived for short term load forecasting. The load model is represented by the state variable form to exploit the Kalman filter techniques. The suggested model has advantages that it is unnecessarty to obtain the coefficients of the harmonic components and its coefficients are not explicitly included in the model. Case studies were carried out for the hourly power demand forecasting of the Korea electrical system.

• Journal of Distribution Science
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• v.10 no.11
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• pp.31-37
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• 2012

Purpose - This study was conducted under the assumption that brand A, a store of company Z of Pangyo, with a new store at Pangyo station is targeting the Bundang-gu area of the newly developed city of Seongnam. Research design, data, methodology - As a result of demand forecasting using geometric series models, an extrapolation of past trends provided the coefficient estimates, without utilizing regression analysis on a constant increase in children's wear, for which the population size and estimated parameter were required. Results - Demand forecasting on the basis of past trends indicates the likelihood that sales of discount stores in the Bundang area, where brand A currently has a presence, would fetch a higher estimated value than that of the average discount store in the country during 2015. If past trends persist, future sales of operational stores are likely to increase. Conclusions - In evaluating location using the simple weighting model, Seohyun Lotte Mart obtained a high rating amongst new stores in Pangyo, on the basis of accessibility, demand class, and existing stores. Therefore, when opening a new counter at a relevant store, a positive effect can be predicted.

• Journal of the Korea Institute of Military Science and Technology
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• v.27 no.3
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• pp.406-415
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• 2024

As the equipment of the military has become more advanced and expensive, the cost of securing spare parts is also constantly increasing along with the increase in equipment assets. In particular, forecasting demand for spare parts one of the important management tasks in the military, and the accuracy of these predictions is directly related to military operations and cost management. However, because the demand for spare parts is intermittent and irregular, it is often difficult to make accurate predictions using traditional statistical methods or a single statistical or machine learning model. In this paper, we propose a model that can increase the accuracy of demand forecasting for irregular patterns of spare parts demanding by using a combination of statistical and machine learning algorithm, and through experiments on Cheonma spare parts demanding data.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.132-140
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• 2019

Recently, many cities around the world introduced and operated shared bicycle system to reduce the traffic and air pollution. Seoul also provides shared bicycle service called as "Ddareungi" since 2015. As the use of shared bicycle increases, the demand for bicycle in each station is also increasing. In addition to the restriction on budget, however, there are managerial issues due to the different demands of each station. Currently, while bicycle rebalancing is used to resolve the huge imbalance of demands among many stations, forecasting uncertain demand at the future is more important problem in practice. In this paper, we develop forecasting model for demand for Seoul shared bicycle using statistical time series analysis and apply our model to the real data. In particular, we apply Holt-Winters method which was used to forecast electricity demand, and perform sensitivity analysis on the parameters that affect on real demand forecasting.

• Journal of Korean Society of Water and Wastewater
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• v.37 no.6
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• pp.363-373
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• 2023

Numerous studies have established a correlation between sociodemographic characteristics and water usage, identifying population as a primary independent variable in mid- to long-term demand forecasting. Recent dramatic sociodemographic changes, including urban concentration-rural depopulation, low birth rates-aging population, and the rise in single-person households, are expected to impact water demand and supply patterns. This underscores the necessity for operational and managerial changes in existing water supply systems. While sociodemographic characteristics are regularly surveyed, the conducted surveys use aggregate units that do not align with the actual system. Consequently, many water demand forecasts have been conducted at the administrative district level without adequately considering the water supply system. This study presents an upward water demand forecasting model that accurately reflects real water facilities and consumers. The model comprises three key steps. Firstly, Statistics Korea's SGIS (Statistical Geological Information System) data was reorganized at the DMA level. Secondly, DMAs were classified using the SOM (Self-Organizing Map) algorithm to consider differences in water facilities and consumer characteristics. Lastly, water demand forecasting employed the PCR (Principal Component Regression) method to address multicollinearity and overfitting issues. The performance evaluation of this model was conducted for DMAs classified as rural areas due to the insufficient number of DMAs. The estimation results indicate that the correlation coefficients exceeded 0.9, and the MAPE remained within approximately 10% for the test dataset. This method is expected to be useful for reorganization plans, such as the expansion and contraction of existing facilities.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.3
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• pp.233-238
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• 2001

This paper deals with demand forecasting of parts in an automobile model which has been extinct. It is important to estimate how much inventory of each part in the extinct model should be stocked because production lines of some parts may be replaced by new ones although there is still demands for the model. Furthermore, in some countries, there is a strong regulation that the automobile manufacturing company should provide customers with auto parts for several years whenever they are requested. The major characteristic of automobile parts demand forecasting is that there exists a close correlation between the number of running cars and the demand of each part. In this sense, the total demand of each part in a year is determined by two factors, the total number of running cars in that year and the failure rate of the part. The total number of running cars in year k can be estimated sequentially by the amount of shipped cars and proportion of discarded cars in years 1, 2,$\cdots$, i. However, it is very difficult to estimate the failure rate of each part because available inter-failure time data is not complete. The failure rate is, therefore, determined so as to minimize the mean squared error between the estimated demand and the observed demand of a part in years 1, 2,$\cdots$, i. In this paper, data obtained from a Korean automobile manufacturing company are used to illustrate our model.

• IE interfaces
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• v.24 no.4
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• pp.373-378
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• 2011

High level of accuracy in forecasting heat demand of each district is required for operating and managing the district heating efficiently. Heat demand has a close connection with the demands of the previous days and the temperature, general demand forecasting methods may be used forecast. However, there are some exceptional situations to apply general methods such as the exceptional low demand in weekends or vacation period. We introduce a new method to forecast the heat demand to overcome these situations, using the linearities between the demand and some other factors. Our method uses the temperature and the past 7 days' demands as the factors which determine the future demand. The model consists of daily and hourly models which are multiple linear regression models. Appling these two models to historical data, we confirmed that our method can forecast the heat demand correctly with reasonable errors.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1282-1289
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• 2011

The rail passenger demand for the railroad operations required a short-term demand rather than a long-term demand. The rail passenger demand can be classified according to the purpose. First, the rail passenger demand will be use to the restructure of line planning on the current operating line. Second, the rail passenger demand will be use to the line planning on the new line and purchasing the train vehicles. The objective of study is to analyze the travel behavior of rail passenger for modeling of short-term demand forecasting. The scope of research is the passenger of KTX. The travel behavior was analyzed the daily trips, origin/destination trips for KTX passenger using the ANOVA and the clustering analysis. The results of analysis provide the directions of the short-term demand forecasting model.

• Journal of Korean Institute of Industrial Engineers
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• v.14 no.1
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• pp.103-117
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• 1988

Replacement demand plays an important role to forecast the total demand of durable goods, while most of the diffusion models deal with only adoption data, namely initial purchase demand. This paper presents replacement demand forecasting models incorporating repurchase rate, multi-ownership, and dynamic product life to complement the existing diffusion models. The performance of replacement demand forecasting models are analyzed and practical guidelines for the application of the models are suggested when life distribution data or adoption data are not available.