• 제목/요약/키워드: forecasting technique

검색결과 353건 처리시간 0.028초

수요경향과 온도를 고려한 1일 최대전력 수요예측 (Daily peak load forecasting considering the load trend and temperature)

  • 최낙훈;손광명;이태기
    • 조명전기설비학회논문지
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    • 제15권6호
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    • pp.35-42
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    • 2001
  • 1일 최대전력 부하 예측 자료는 계통의 경제적 운용과 전력 감시에 필수적이므로 정확한 예측기법이 요구된다. 신경회로망이나 퍼지이론을 한 예측비법의 장점은 정도(精度)가 높고 운용하기가 편리한 점은 있으나 학습시간이 길고, 부하가 급변할 때는 예측오차가 크게 발생한다. 본 연구에서는 이러한 단점을 개선하기 위하여 새로운 예측 기법을 제시하였으며 예측결과에서 타당성이 입증되었다.

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온도를 고려한 지수평활에 의한 단기부하 예측 (Short-Term Load Forecasting Exponential Smoothoing in Consideration of T)

  • 고희석;이태기;김현덕;이충식
    • 대한전기학회논문지
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    • 제43권5호
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    • pp.730-738
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    • 1994
  • The major advantage of the short-term load forecasting technique using general exponential smoothing is high accuracy and operational simplicity, but it makes large forecasting error when the load changes repidly. The paper has presented new technique to improve those shortcomings, and according to forecasted the technique proved to be valid for two years. The structure of load model is time function which consists of daily-and temperature-deviation component. The average of standard percentage erro in daily forecasting for two years was 2.02%, and this forecasting technique has improved standard erro by 0.46%. As relative coefficient for daily and seasonal forecasting is 0.95 or more, this technique proved to be valid.

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기온변화에 의한 수요변동을 고려한 단기 전력수요예측 전문가시스템의 연구 (A study on the short-term load forecasting expert system considering the load variations due to the change in temperature)

  • 김광호;이철희
    • 산업기술연구
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    • 제15권
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    • pp.187-193
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    • 1995
  • In this paper, a short-term load forecasting expert system considering the load variation due to the change in temperature is presented. The change in temperature is an important load variation factor that varies the normal load pattern. The conventional load forecasting methods by artificial neural networks have used the technique where the temperature variables were included in the input neurons of artificial neural networks. However, simply adding the input units of temperature data may make the forecasting accuracy worse, since the accuracy of the load forecasting in this method depends on the accuracy of weather forecasting. In this paper, the fuzzy expert system that modifies the forecasted load using fuzzy rules representing the relations of load and temperature is presented and compared with a conventional load forecasting technique. In the test case of 1991, the proposed model provided a more accurate forecast than the conventional technique.

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Airline In-flight Meal Demand Forecasting with Neural Networks and Time Series Models

  • Lee, Young-Chan
    • 한국정보시스템학회:학술대회논문집
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    • 한국정보시스템학회 2000년도 추계학술대회
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    • pp.36-44
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    • 2000
  • The purpose of this study is to introduce a more efficient forecasting technique, which could help result the reduction of cost in removing the waste of airline in-flight meals. We will use a neural network approach known to many researchers as the “Outstanding Forecasting Technique”. We employed a multi-layer perceptron neural network using a backpropagation algorithm. We also suggested using other related information to improve the forecasting performances of neural networks. We divided the data into three sets, which are training data set, cross validation data set, and test data set. Time lag variables are still employed in our model according to the general view of time series forecasting. We measured the accuracy of our model by “Mean Square Error”(MSE). The suggested model proved most excellent in serving economy class in-flight meals. Forecasting the exact amount of meals needed for each airline could reduce the waste of meals and therefore, lead to the reduction of cost. Better yet, it could enhance the cost competition of each airline, keep the schedules on time, and lead to better service.

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중회귀식을 이용한 원주시 $SO_2$ 오염도 예보기법 개발에 관한 연구 (On the Development of the Statistical $SO_2$ Forecasting Technique by the Multiple Regression Analysis in Wonju City)

  • 송동웅
    • 한국환경과학회지
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    • 제7권6호
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    • pp.827-831
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    • 1998
  • Statistical $SO_2$ forecasting technique by multiple regression analysis was designed and developed to predict $SO_2$ concentration in Wonju City. $SO_2$ concentration data measured from air pollution monitoring system and meteorological factors data such as : wind speed, atmospheric stability, surface temperature, relative humidity and precipitation were used in Wonju City during the 1996~1997. As the results, correlation model for forecasting was well fitted with some parameters including minimum temperature, wind speed and the $SO_2$ concentration of the previous day.

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단변량 시계열모형을 이용한 식음료 수요예측에 관한 연구 - 서울소재 특1급 H호텔 사례를 중심으로 - (Forecasting Demand for Food & Beverage by Using Univariate Time Series Models: - Whit a focus on hotel H in Seoul -)

  • 김석출;최수근
    • 한국조리학회지
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    • 제5권1호
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    • pp.89-101
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    • 1999
  • This study attempts to identify the most accurate quantitative forecasting technique for measuring the future level of demand for food & beverage in super deluxe hotel in Seoul, which will subsequently lead to determining the optimal level of purchasing food & beverage. This study, in detail, examines the food purchasing system of H hotel, reviews three rigorous univariate time series models and identify the most accurate forecasting technique. The monthly data ranging from January 1990 to December 1997 (96 observations) were used for the empirical analysis and the 1998 data were left for the comparison with the ex post forecast results. In order to measure the accuracy, MAPE, MAD and RMSE were used as criteria. In this study, Box-Jenkins model was turned out to be the most accurate technique for forecasting hotel food & beverage demand among selected models generating 3.8% forecast error in average.

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지연된 국방 연구개발 프로젝트의 일정 예측방식 개선 연구 (Research on Improving Schedule Forecasting Method for Delayed Defense Research & Development Project)

  • 조정호;임재성
    • 한국군사과학기술학회지
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    • 제23권3호
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    • pp.286-293
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    • 2020
  • Since Dr. Lipke announces earned schedule management(ESM) in 2002, it has been used in project management to make up for the insufficient schedule management function of earned value management technique. However, it is difficult to accurately forecast the schedule of delayed defense research and development(R&D) projects with the ESM technique. Therefore, this paper proposes a new schedule forecasting method considering the progress of delayed work in ESM technique. This concept can also be adopted to the traditional project progress management (PPM) technique. We verify the effectiveness of the proposed concept through several defense R&D projects and prove that it is possible to supplement the schedule forecasting of the ESM and PPM technique.

순환형식에 의한 기분거좌상측 알고리 (A New Algorithm for Recursive Short-term Load Forecasting)

  • Young-Moon Park;Sung-Chul Oh
    • 대한전기학회논문지
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    • 제32권5호
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    • pp.183-188
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    • 1983
  • This paper deals with short-term load forecasting. The load model is represented by the state variable form to exploit the Kalman filter technique. The load model is derived from Taylor series expansion and remainder term is considered as noise term. In order to solve recursive filter form, among various algorithm of solving Kalman filter, this paper uses exponential data weighting technique. This paper also deals with the asymptotic stability of filter. Case studies are carried out for the hourly power demand forecasting of the Korea electrical system.

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호텔 객실판매 예측에 관한 실증적 연구 - 서울지역 특급호텔을 중심으로 - (Empirical Study on the Forecasting of the Hotel Room Sales)

  • 한승엽
    • 산학경영연구
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    • 제4권
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    • pp.281-295
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    • 1991
  • Nothing is more incorrect than forecasting. Nevertheless, forecasting is one of the most important business activities for the effective management. There has been rapid changes of the growth rate in every respect of the Korean hospitaity industry, especially the hotel industry, before and after the 88 Olympic Games. Therefore, the hoteliers shall be in need of more-than-ever accourate demand forecasting for the more systematic management and control. Under the above circumstances, this study suggested the best forecasting technique and method for the better sales and operations of the hotel rooms. The number of rooms sold is selected as a dependent variable of this study which is regarded as the best representative factor of measuring the growth rate of the rooms division performance of the hotels. The first step was to select the most verifiable independent variable diferently from the other countries or other areas of Korea. As a result, the number of foreign visitors was chosen. Empirical research, i.e. correlation and multiple regression analysis, shows that this independent variable has a strong relationship with the dependent variable told above. The second procedure was to estimate the number of rooms will be sold in 1991 on the basis of the formula calculated through the multiple regression analysis. Time series technique was conducted using the data of the number of foreign visitors by purpose of travel from 1987 to 1990. For the more correct forecasting, however, it would be desirable to adopt the data from 1989 considering the product or the industry life cycle. In addition, deeper analysis for the monthly or seasonal forecasting method is needed as a future research.

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특수일 최대 전력 수요 예측을 위한 결정계수를 사용한 데이터 마이닝 (Data Mining Technique Using the Coefficient of Determination in Holiday Load Forecasting)

  • 위영민;송경빈;주성관
    • 전기학회논문지
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    • 제58권1호
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    • pp.18-22
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    • 2009
  • Short-term load forecasting (STLF) is an important task in power system planning and operation. Its accuracy affects the reliability and economic operation of power systems. STLF is to be classified into load forecasting for weekdays, weekends, and holidays. Due to the limited historical data available, it is more difficult to accurately forecast load for holidays than to forecast load for weekdays and weekends. It has been recognized that the forecasting errors for holidays are large compared with those for weekdays in Korea. This paper presents a polynomial regression with data mining technique to forecast load for holidays. In statistics, a polynomial is widely used in situations where the response is curvilinear, because even complex nonlinear relationships can be adequately modeled by polynomials over a reasonably small range of the dependent variables. In the paper, the coefficient of determination is proposed as a selection criterion for screening weekday data used in holiday load forecasting. A numerical example is presented to validate the effectiveness of the proposed holiday load forecasting method.