• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.646-648
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• 1999

Kalman Filter model of demand for residental water and consumption pattern were tested for their ability to explain the hourly residental demand for water in metropolitan distribution system. The hourly residental demand for water is calculated from the daily residental demand and consumption pattern. The consumption pattern which has 24 time rates is characterized by data granulization in accordance with season kind, weather and holiday. The proposed approach is applied to water distribution system of metropolitan areas in Korea and its effectiveness is checked.

• The Journal of Information Systems
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• v.31 no.3
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• pp.197-220
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• 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.

• Proceedings of the KIEE Conference
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• 2001.05a
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• pp.89-91
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• 2001

This paper presents the result of pattern classification of load demand for distribution transformer in domestic. The field data of load demand is measured using the load acquisition device and the measurement data is used for the database system for load management of distribution transformed. For the pattern classification, the load data and the customer information data are also used. The K-MEAN method is used for the pattern classification algorithm. The result of pattern classification is used for the 2-step format of load demand curve.

• Journal of Korea Society of Industrial Information Systems
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• v.18 no.3
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• pp.35-46
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• 2013

In order to develop an accurate analytical model for domestic electricity demand forecasting, we propose a prediction method of the electric power demand pattern by combining SMO classification techniques and a dimension reduction conceptualized subspace clustering techniques suitable for high-dimensional data cluster analysis. In terms of electricity demand pattern prediction, hourly electricity load patterns and the demographic and geographic characteristics can be analyzed by integrating the wireless load monitoring data as well as sub-regional unit of census information. There are composed of a total of 18 characteristics clusters in the prediction result for the sub-regional demand pattern by using census information and power load of Seoul metropolitan area. The power demand pattern prediction accuracy was approximately 85%.

• 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 Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.9
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• pp.1486-1492
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• 2016

This study presents the electrical load forecasting and error correction method using a real building load pattern, and the way to manage the energy storage system with forecasting results for economical load operation. To make a unique pattern of target load, we performed the Hierarchical clustering that is one of the data mining techniques, defined load pattern(group) and forecasted the demand load according to the clustering result of electrical load through the previous study. In this paper, we propose the new reference demand for improving a predictive accuracy of load demand forecasting. In addition we study an error correction method for response of load events in demand load forecasting, and verify the effects of proposed correction method through EMS scheduling simulation with load forecasting correction.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.15 no.2
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• pp.38-48
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• 2001

Kalman Filter model of demand for residental water and consumption pattern wore tested for their ability to explain the hourly residental demand for water in metro-politan distribution system. The daily residental demand can be obtained from Kalman Filter model which is optimized by statistical analysis of input variables. The hourly residental demand for water is calculated from the daily residental demand and consumption pattern. The consumption pattern which has 24 time rates is characterized by data granulization in accordance with season kind, weather and holiday. The proposed approach is applied to water distribution system of metropolitan areas in Korea and its effectiveness is checked.

• Proceedings of the IEEK Conference
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• 2009.05a
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• pp.287-289
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• 2009

The conventional computing is a centralizing system, but it has been gradually going to develop ubiquitous computing which moves roles away from the main. The Community Computing, a new paradigm, is proposed to implement environment of ubiquitous computing. In this environment, it is important to accept the user demand. Hence in this paper recognizes pattern of user's activity statistically and proposes a method of pattern estimation in community computing. In addition, user's activity varies with time and the activity has the priority We reflect these. Also, we improve accuracy of the method through Knowledge Base organization and the feedback system. We make program using Microsoft Visual C++ for evaluating performance of proposed method, then simulate it. We can confirm it from the experiment result that using proposal method is better in environment of community computing.

• Journal of Information Technology Applications and Management
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• v.24 no.2
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• pp.81-96
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• 2017

Forecasting long-term mobile service demand is inevitable to establish an effective frequency management policy despite the lack of reliability of forecast results. The statistical forecasting method has limitations in analyzing how the forecasting result changes when the scenario for various drivers such as consumer usage pattern or market structure for mobile communication service is changed. In this study, we propose a dynamic model of the mobile communication service market using system dynamics technique and forecast the future demand for long-term mobile communication subscriber based on the dynamic model, and also experiment on the change pattern of subscriber demand under various scenarios.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.11
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• pp.1956-1963
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• 2010

Studies on the development of various energy management programs and real-time bidirectional information infrastructures have been actively conducted to promote the reduction of power demands and CO2 emissions effectively. In the conventional energy management programs, the demand response program that can transition or transfer the power use spontaneously for power prices and other signals has been largely used throughout the inside and outside of the country. For measuring the effect of such demand response program, it is necessary to exactly estimate short-term loads. In this study, the power consumption patterns in both individual and group consumers were analyzed to estimate the exact short-term loads, and the relationship between the actual power consumption and seasonal factors was also analyzed.