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

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

• 한국산학기술학회논문지
• /
• 제16권10호
• /
• pp.6860-6868
• /
• 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
• /
• 제18권6호
• /
• pp.758-769
• /
• 2004

The trends and seasonalities of most time series have a large variability. The result of the Singular Spectrum Analysis(SSA) processing is a decomposition of the time series into several components, which can often be identified as trends, seasonalities and other oscillatory series, or noise components. Generally, forecasting by the SSA method should be applied to time series governed (may be approximately) by linear recurrent formulae(LRF). This study examined forecasting ability of SSA-LRF model. These methods are applied to daily water demand data. These models indicate that most cases have good ability of forecasting to some extent by considering statistical and visual assessment, in particular forecasting validity shows good results during 15 days.

• 산업공학
• /
• 제24권4호
• /
• pp.373-378
• /
• 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.

• 전기학회논문지
• /
• 제65권7호
• /
• pp.1115-1120
• /
• 2016

This paper presents the daily load forecasting for special days considering the rate of operation of industrial consumers. The authors analyzed the power consumption pattern for both the special and ordinary days according to the contract power classification of industrial consumers, and selected 400~600 specific consumers for which the rates of operation during special days are needed. Load forecasting for 2014 special days considering the rate of operation of industrial consumers showed a noticeable improvement on forecasting error of daily peak demand, which proved the effectiveness of the survey for the rates of operation during special days of industrial consumers.

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

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2004년도 학술발표회
• /
• pp.709-712
• /
• 2004

The development of a basin-wide runoff analysis model is to analysis monthly and daily hydrologic runoff components including surface runoff, subsurface runoff, return flow, etc. at key operation station in the targeted basin. h short-term water demand forecasting technology will be developed fatting into account the patterns of municipal, industrial and agricultural water uses. For the development and utilization of runoff analysis model, relevant basin information including historical precipitation and river water stage data, geophysical basin characteristics, and water intake and consumptions needs to be collected and stored into the hydrologic database of Integrated Real-time Water Information System. The well-known SSARR model was selected for the basis of continuous daily runoff model for forecasting short and long-term natural flows.

• 한국철도학회:학술대회논문집
• /
• 한국철도학회 2011년도 춘계학술대회 논문집
• /
• pp.1282-1289
• /
• 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.

• 한국간호교육학회지
• /
• 제15권2호
• /
• pp.251-259
• /
• 2009

Purpose: Climate change affects human health and calls for a health forecasting service. The purpose of this study was to explore the students' actual use and their satisfaction with meteorological information and the demands on health forecasting at a university in South Kyungsang Province. Method: This study used a descriptive design through structured self-report questionnaires including frequency, contents, purpose, perception, satisfaction of meterological information and need and demand of health forecasting. Data were collected from June 1 to 5, 2009 and analyzed using the SPSS 17.0 program. Descriptive statistics, t-test, ANOVA, $\chi^2$ test and Person's correlation coefficient were used to analyze the data. Result: The majority of the students watched the daily weather information to decide about daily work, outdoor activity or habitually. The mean score of need for health forecasting was $3.44{\pm}.81$, and the demand for health forecasting was $2.93{\pm}1.05$. Significant differences were found in the need for health forecasting according to sex, major, and environmental disease. In addition, the higher the satisfaction of health forecasting, the higher the demand for it. Conclusion: I suggest improving the meteorological information system technically and developing a health forecasting service resulting in a healthier and more comfortable life.