• Journal of Computing Science and Engineering
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• 제8권1호
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• pp.17-24
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• 2014

In this paper, we propose a new implementation of a failure detector. The implementation uses a dual model of heartbeat and interaction. First, the heartbeat model is adopted to shorten the detection time, if the detection process does not receive the heartbeat message in the expected time. The interaction model is then used to check the process further. The expected time is calculated using the exponential smoothing method. Exponential smoothing can be used to estimate the next arrival time not only in the random data, but also in the data of linear trends. It is proven that the new detector in the paper can eventually be a perfect detector.

• 대한전기학회논문지:전력기술부문A
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• 제53권10호
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• pp.529-535
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• 2004

Load forecasting is essential in the electricity market for the participants to manage the market efficiently and stably. A wide variety of techniques/algorithms for load forecasting has been reported in many literatures. These techniques are as follows: multiple linear regression, stochastic time series, general exponential smoothing, state space and Kalman filter, knowledge-based expert system approach (fuzzy method and artificial neural network). These techniques have improved the accuracy of the load forecasting. In recent 10 years, many researchers have focused on artificial neural network and fuzzy method for the load forecasting. In this paper, we propose an algorithm of a hybrid load forecasting method using fuzzy linear regression and general exponential smoothing and considering the sensitivities of the temperature. In order to consider the lower load of weekends and Monday than weekdays, fuzzy linear regression method is proposed. The temperature sensitivity is used to improve the accuracy of the load forecasting through the relation of the daily load and temperature. And the normal load of weekdays is easily forecasted by general exponential smoothing method. Test results show that the proposed algorithm improves the accuracy of the load forecasting in 1996.

• 응용통계연구
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• 제33권3호
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• pp.257-267
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• 2020

본 논문은 단변량 지수평활법의 확장된 형태인 다변량 지수평활법을 소개하고 다변량 시계열 분석에 활용한다. 다변량 지수평활법은 한 개의 오차를 기반으로 하는 상태공간모형을 이용하여 추정의 편리성을 제고하며, 다변량 시계열간의 잠재적인 상호연관성을 활용하여 적합도 및 예측력을 향상시킨다. 다변량 지수평활법의 성능을 평가하기 위하여 월별 원/달러 및 원/파운드 환율자료를 분석하고 예측한다. 대안 모형의 예측 결과와 비교하여 다변량 지수평활법의 우수성을 확인한다.

• 한국컴퓨터정보학회논문지
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• 제11권5호
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• pp.59-67
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• 2006

본 논문에서는 지수평활을 이용한 법원경매 정보 시스템의 낙찰가 예측 방법을 제안하였다. 이 시스템은 권리분석을 위하여 낙찰가를 예측하고, 낙찰예측가에 따라 배당 정보를 제공하도록 설계되어 있으며 이를 구현하기 위하여 물건 자료의 입력 인터페이스와 정보 제공을 위한 웹 인터페이스를 구축하였다. 자료 입력 인터페이스는 자료의 입력, 수정, 삭제 기능을 가지며, 웹 인터페이스는 법원경매 물건을 중심으로 관련 정보를 제공한다. 실시간 정보 제공에 초점을 두고 자동 권리분석이 가능하도록 하기 위하여 낙찰가를 시계열 자료로 표현하여 지수평활을 이용한 낙찰예상가를 예측하는 방법을 제안하고, 기존의 방법과 비교 실험을 통하여 제안방법의 유효성을 검증한다.

• 전기학회논문지
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• 제62권2호
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• pp.149-154
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• 2013

Short term load forecasting for electric power demand is essential for stable power system operation and efficient power market operation. High accuracy of the short term load forecasting can keep the power system more stable and save the power market operation cost. We propose an optimal coefficient selection method for exponential smoothing model in short term load forecasting on weekdays. In order to find the optimal coefficient of exponential smoothing model, load forecasting errors are minimized for actual electric load demand data of last three years. The proposed method are verified by case studies for last three years from 2009 to 2011. The results of case studies show that the average percentage errors of the proposed load forecasting method are improved comparing with errors of the previous methods.

• 산업경영시스템학회지
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• 제42권1호
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• pp.129-136
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• 2019

In this study, we proposed a model for forecasting power energy demand by investigating how outside temperature at a given time affected power consumption and. To this end, we analyzed the time series of power consumption in terms of the power spectrum and found the periodicities of one day and one week. With these periodicities, we investigated two time series of temperature and power consumption, and found, for a given hour, an approximate linear relation between temperature and power consumption. We adopted an exponential smoothing model to examine the effect of the linearity in forecasting the power demand. In particular, we adjusted the exponential smoothing model by using the variation of power consumption due to temperature change. In this way, the proposed model became a mixture of a time series model and a regression model. We demonstrated that the adjusted model outperformed the exponential smoothing model alone in terms of the mean relative percentage error and the root mean square error in the range of 3%~8% and 4kWh~27kWh, respectively. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric energy together with the outside temperature.

• 응용통계연구
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• 제22권1호
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• pp.221-235
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• 2009

This manuscript illustrates the comparative study between ARIMA and Exponential Smoothing modeling to develop forest fire forecasting system using different weather parameters. In this paper, authors have developed the most suitable and closest forecasting models like ARIMA and Exponential Smoothing techniques using different weather parameters. Authors have considered the extremes of the Wind speed, Radiation, Maximum Temperature and Deviation Temperature of the Summer Season form March to June month for the Ranchi Region in Jharkhand. The data is taken by own resource with the help of Automatic Weather Station. This paper consists a deep study of the effect of extreme values of the different parameters on the weather fluctuations which creates forest fires in the region. In this paper, the numerical illustration has been incorporated to support the present study. Comparative study of different suitable models also incorporated and best fitted model has been tested for these parameters.

• 산업공학
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• 제18권3호
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• pp.343-349
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• 2005

This study presents the empirical results of comparing several demand forecasting methods for Supply Chain Management(SCM). Adaptive exponential smoothing using change detection statistics (Jun) is compared with Trigg and Leach's adaptive methods and SAS time series forecasting systems using weekly SCM demand data. The results show that Jun's method is superior to others in terms of one-step-ahead forecast error and eight-step-ahead forecast error. Based on the results, we conclude that the forecasting performance of SCM solution can be improved by the proposed adaptive forecasting method.

• 한국컴퓨터정보학회논문지
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• 제10권1호
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• pp.45-52
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• 2005

본 연구에서는 시간 연관 규칙에 지수 평활법을 적용한 상품 추천 알고리즘을 제안한다. 시간 연관 규칙은 기존의 연관 규칙에 시간 개념을 적용한 연관 규칙이다. 본 연구에서는 과거 데이터 보다 최신의 데이터에 가중치를 더 부여한 지수 평활 시간 연관 규칙을 제안한다. 제안한 알고리즘은 시간 의존적인 데이터에 적용하여 시뮬레이션을 한 결과 지수 평활법을 적용한 시간 연관 규칙이 기존의 시간 연관 규칙보다 실행시간 면에서 다소 오래 걸리지만 상품 추천 측면에서 더 효과적이다.

• Journal of the Korean Data and Information Science Society
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• 제19권4호
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• pp.1133-1140
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• 2008

The goal of this paper is to analysis of the trend for stream quality in Gumi industrial complex with Winters' exponential smoothing method. It used the five different monthly time series data such as BOD, COD, TN, TP and EC from January 1998 to December 2006. The data of BOD, COD, TN, TP and EC are analyzed by time series method and forecasted the trends until December 2007. The stream qualities change for the better about BOD, COD, TN and TP, but the stream qualities resulted by EC is still serious.