• 대한산업공학회지
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• 제39권4호
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• pp.247-252
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• 2013

Korea gas corporation (KOGAS) is responsible for the whole sale of natural gas in the domestic market. It is important to forecast the daily demand of city gas for supply and demand control, and delivery management. Since there is the autoregressive characteristic in the daily gas demand, we introduce a modified autoregressive model as the first step. The daily gas demand also has a close connection with the outdoor temperature. Accordingly, our second proposed model is a temperature-based model. Those two models, however, do not meet the requirement for forecasting performances. To produce acceptable forecasting performances, we develop a weighted average model which compounds the autoregressive model and the temperature model. To examine our proposed methods, the forecasting results are provided. We confirm that our method can forecast the daily city gas demand accurately with reasonable performances.

• 전기학회논문지P
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• 제63권3호
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• pp.189-194
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• 2014

Due to the increasing of power consumption, it is difficult to construct accurate prediction model for daily peak power demand. It is very important work to know power demand in next day for manager and control power system. In this research, we develop a daily peak power demand prediction method based on hybrid type composed of AR and Neuro-Fuzzy model. Using data sets between 2006 and 2010 in Korea, the proposed method has been intensively tested. As the prediction results, we confirm that the proposed method makes it possible to effective estimate daily peak power demand than conventional methods.

• 전기학회논문지P
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• 제63권4호
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• pp.307-311
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• 2014

Due to the increasing of power consumption, it is difficult to construct accurate prediction model for daily peak power demand. It is very important work to know power demand in next day for manager and control power system. In this research, we develop a daily peak power demand prediction method considering of characteristics of day of week. The proposed method is composed of liner model based on AR model and nonlinear model based on ELM to resolve the limitation of a single model. Using data sets between 2006 and 2010 in Korea, the proposed method has been intensively tested. As the prediction results, we confirm that the proposed method makes it possible to effective estimate daily peak power demand than conventional methods.

• 전기학회논문지P
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• 제62권4호
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• pp.169-174
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• 2013

Due to the increase of power consumption, it is difficult to construct an accurate prediction model for daily peak power demand. It is very important work to know power demand in next day to manage and control power system. In this research, we develop a daily peak power demand prediction method based on Extreme Learning Machine(ELM) with fast learning procedure. Using data sets between 2006 and 2010 in Korea, the proposed method has been intensively tested. As the prediction results, we confirm that the proposed method makes it possible to effective estimate daily peak power demand than conventional methods.

• 자원ㆍ환경경제연구
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• 제29권4호
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• pp.419-442
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• 2020

우리나라 도시가스 수요는 난방수요에 기인한 뚜렷한 동고하저의 계절성을 보이며, 기온에 따른 민감도는 시간에 따라 변화하는 것으로 나타났다. 본 연구에서는 시간에 따라 변화하는 계절성을 효과적으로 모형하기 위해서 시간변동 기온반응함수 개념을 도입하여 이를 해당 일의 기온분포로 적분하여 기온에 따른 수요변동을 추정한다. 또한 기상청에서 발표하는 향후 10일의 도시별 기온 예측치를 체계적으로 반영하여 도시가스 수요를 예측하는 방법론을 개발하였다. 평년기온분포를 사용한 것에 비해서 함수적 방법론을 이용하여 기상청의 기온 예측치를 기온분포예측치로 변환하여 예측했을 때 기온분포의 예측 오차율은 2배, 도시가스 수요의 예측 오차는 5배 가까이 감소하는 것을 확인하였다.

• 한국산학기술학회논문지
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• 제16권10호
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• pp.6860-6868
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• 2015

본 연구에서는 국내 도시가스 일일 수요 예측에 대한 문제를 다룬다. 정확한 일일 수요 예측은 안정적인 도시가스의 수급을 위해서 필수적인 사항으로 실제 가스 공급기관의 일상 업무에 해당한다. 본 연구에서는 수요예측 방법을 고안하기 위하여 일일 도시가스 수요 시계열에 대한 데이터 분석을 수행하였으며, 예측일 수요에 영향을 주는 주요한 요인으로 직전일 수요, 기온, 요일 등을 파악하였다. 본 연구에서는 이러한 요인들을 고려한 회귀 모형과 국내 도시가스 수요 특성에 맞는 선별적 샘플링 절차를 제안하였다. 제안 모형과 선별적 샘플링 절차로 구성된 예측 방법의 성능 검증을 위하여 실제 도시가스 수요에 대한 예측을 수행하였다. 문헌에 소개된 기존 방법과 예측 성능을 비교한 결과, 본 연구에서 제안한 방법의 평균절대백분율오차는 약 2.22%로서 개선 비율은 대략 7%에 해당한다.

• 상하수도학회지
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• 제30권3호
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• pp.225-231
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• 2016

Peak load rate(i.e., maximum daily flow/average daily flow) has not been considered for industrial water demand planning in Korea to date, while area unit method based on average daily flow has been applied to decide capacity of industrial water treatment plants(WTPs). Designers of industrial WTPs has assumed that peak load would not exist if operation rate of factories in industrial sites were close to 100%. However, peak load rates were calculated as 1.10~2.53 based on daily water flow from 2009 to 2014 for 9 industrial WTPs which have been operated more than 9 years(9-38 years). Furthermore, average operation rates of 9 industrial WTPs was less than 70% which means current area unit method has tendency to overestimate water demand. Therefore, it is not reasonable to consider peak load for the calculation of water demand under current area unit method application to prevent overestimation. However, for the precise future industrial water demand calculation more precise data gathering for average daily flow and consideration of peak load rate are recommended.

• 한국정보시스템학회지:정보시스템연구
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• 제31권3호
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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.

• 동의생리병리학회지
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• 제20권3호
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• pp.527-534
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• 2006

This is a pilot study to survey the general demand of senior-assistive necessities before a standardization system for senior-assistive necessities is developed as well as to describe health professionals' opinions about the demand and quality of Oriental medical aids and necessities for daily living for the elderly. This is a descriptive survey in which 29 health professionals are questioned, using structured questionnaires based on ISO 9999. The questionnaires were developed by 7 expert conduction standardization system of senior-assistive products in Korea. The data is analyzed by descriptive statistics. The result is as follows : First, with regard to the demand for all of the items in Oriental medical aids for the elderly, the demand of cupping glasses is the highest, followed by instrument used to apply heat treatment, massage equipment, thermo-therapeutic mattress, and heat or ice packs. With regard to the demand for all of the items for the necessities for daily living for the elderly, chairs are the highest, followed by rolling chairs, beds for health, and heigh adjustable beds. Second, with regard to quality of Oriental medical aids, ${\ulcorner}$aids for hair care${\lrcorner}$ are the best, whereas ${\ulcorner}$aids for boiling Oriental medicine${\lrcorner}$ are the worst. In quality of the necessities for daily living, ${\ulcorner}$chairs${\lrcorner}$ are the best, whereas ${\ulcorner}$beds${\lrcorner}$ are the worst. Above all, this result shows that with ${\ulcorner}$aids for heat or cold treatment${\lrcorner}$, there is relatively high demand and low quaily of Oriental medical aids, and with ${\ulcorner}$Beds${\lrcorner}$, there is relatively high demand and low quality. Therefore, aids for heat or cold treatment and beds in th necessities for daily living are required to be developed for standardization of senior-assistive necessities.

• 산업경영시스템학회지
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• 제39권1호
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• pp.25-30
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• 2016

It is critical to forecast the maximum daily and monthly demand for power with as little error as possible for our industry and national economy. In general, long-term forecasting of power demand has been studied from both the consumer's perspective and an econometrics model in the form of a generalized linear model with predictors. Time series techniques are used for short-term forecasting with no predictors as predictors must be predicted prior to forecasting response variables and containing estimation errors during this process is inevitable. In previous researches, seasonal exponential smoothing method, SARMA (Seasonal Auto Regressive Moving Average) with consideration to weekly pattern Neuron-Fuzzy model, SVR (Support Vector Regression) model with predictors explored through machine learning, and K-means clustering technique in the various approaches have been applied to short-term power supply forecasting. In this paper, SARMA and intervention model are fitted to forecast the maximum power load daily, weekly, and monthly by using the empirical data from 2011 through 2013. $ARMA(2,\;1,\;2)(1,\;1,\;1)_7$ and $ARMA(0,\;1,\;1)(1,\;1,\;0)_{12}$ are fitted respectively to the daily and monthly power demand, but the weekly power demand is not fitted by AREA because of unit root series. In our fitted intervention model, the factors of long holidays, summer and winter are significant in the form of indicator function. The SARMA with MAPE (Mean Absolute Percentage Error) of 2.45% and intervention model with MAPE of 2.44% are more efficient than the present seasonal exponential smoothing with MAPE of about 4%. Although the dynamic repression model with the predictors of humidity, temperature, and seasonal dummies was applied to foretaste the daily power demand, it lead to a high MAPE of 3.5% even though it has estimation error of predictors.