• Title/Summary/Keyword: Load forecasting

Search Result 302, Processing Time 0.019 seconds

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

  • 고희석;이태기;김현덕;이충식
    • The Transactions of the Korean Institute of Electrical Engineers
    • /
    • v.43 no.5
    • /
    • pp.730-738
    • /
    • 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.

  • PDF

Weekly maximum power demand forecasting using model in consideration of temperature estimation (기온예상치를 고려한 모델에 의한 주간최대전력수요예측)

  • 고희석;이충식;김종달;최종규
    • The Transactions of the Korean Institute of Electrical Engineers
    • /
    • v.45 no.4
    • /
    • pp.511-516
    • /
    • 1996
  • In this paper, weekly maximum power demand forecasting method in consideration of temperature estimation using a time series model was presented. The method removing weekly, seasonal variations on the load and irregularities variation due to unknown factor was presented. The forecasting model that represent the relations between load and temperature which get a numeral expected temperature based on the past 30 years(1961~1990) temperature was constructed. Effect of holiday was removed by using a weekday change ratio, and irregularities variation was removed by using an autoregressive model. The results of load forecasting show the ability of the method in forecasting with good accuracy without suffering from the effect of seasons and holidays. Percentage error load forecasting of all seasons except summer was obtained below 2 percentage. (author). refs., figs., tabs.

  • PDF

A new approach to short term load forecasting (전력계통부하예측에 관한 연구)

  • 양흥석
    • 전기의세계
    • /
    • v.29 no.4
    • /
    • pp.260-264
    • /
    • 1980
  • In this paper, a new algorithm is derived for short term load forecasting. The load model is represented by the state variable form to exploit the Kalman filter techniques. The suggested model has advantages that it is unnecessarty to obtain the coefficients of the harmonic components and its coefficients are not explicitly included in the model. Case studies were carried out for the hourly power demand forecasting of the Korea electrical system.

  • PDF

Representative Temperature Assessment for Improvement of Short-Term Load Forecasting Accuracy (단기 전력수요예측 정확도 개선을 위한 대표기온 산정방안)

  • Lim, Jong-Hun;Kim, Si-Yeon;Park, Jeong-Do;Song, Kyung-Bin
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
    • /
    • v.27 no.6
    • /
    • pp.39-43
    • /
    • 2013
  • The current representative temperature selection method with five cities cannot reflect the sufficient regional climate characteristics. In this paper, the new representative temperature selection method is proposed with the consideration of eight representative cities. The proposed method considered the recent trend of power sales, the climate characteristics and population distribution to improve the accuracy of short-term load forecasting. Case study results for the accuracy of short-term load forecasting are compared for the traditional temperature weights of five cities and the proposed temperature weights of eight cities. The simulation results show that the proposed method provides more accurate results than the traditional method.

Short-Term Load Forecasting using Multiple Time-Series Model (다변수 시계열 분석에 의한 단기부하예측)

  • Lee, Kyung-Hun;Lee, Yun-Ho;Kim, Jin-O;Lee, Hyo-Sang
    • Proceedings of the KIEE Conference
    • /
    • 2001.05a
    • /
    • pp.230-232
    • /
    • 2001
  • This paper presents a model for short-term load forecasting using multiple time-series. We made one-hour ahead load forecasting without classifying load data according to daily load patterns(e.g. weekday. weekend and holiday) To verify its effectiveness. the results are compared with those of neuro-fuzzy forecasting model(5). The results show that the proposed model has more accurate estimate in forecasting.

  • PDF

Long-term Load Forecasting considering economic indicator (경제지표를 고려한 장기전력부하예측 기법)

  • Choi, Sang-Bong;Kim, Dae-Kyeong;Jeong, Seong-Hwan;Bae, Jeong-Hyo;Ha, Tae-Hwan;Lee, Hyun-Goo;Lee, Kang-Sae
    • Proceedings of the KIEE Conference
    • /
    • 1998.07c
    • /
    • pp.1163-1165
    • /
    • 1998
  • This paper presents a method of the regional long-term load forecasting considering economic indicator with the assuption that energy demands proportionally increases with the economic indicators. For the accurate load forecasting, it is very important to scrutinize the correlation among the regional electric power demands, economic indicator and other characteristics because load forecasting results may vary depending on many different factors such as electric power demands, gross products, social trend and so on. Three steps are microscopically and macroscopically used for the regional long-term load forecasting in order to increase the accuracy and practically of the results.

  • PDF

SVM Load Forecasting using Cross-Validation (교차검증을 이용한 SVM 전력수요예측)

  • Jo, Nam-Hoon
    • The Transactions of the Korean Institute of Electrical Engineers A
    • /
    • v.55 no.11
    • /
    • pp.485-491
    • /
    • 2006
  • In this paper, we study the problem of model selection for Support Vector Machine(SVM) predictor for short-term load forecasting. The model selection amounts to tuning SVM parameters, such as the cost coefficient C and kernel parameters and so on, in order to maximize the prediction performance of SVM. We propose that Cross-Validation method can be used as a model selection algorithm for SVM-based load forecasting technique. Through the various experiments on several data sets, we found that the difference between the prediction error of SVM using Cross-Validation and that of ideal SVM is less than 5%. This shows that SVM parameters for load forecasting can be efficiently tuned by using Cross-Validation.

Development of Neural Network System for Short-Term Load Forecasting (특수일 전력수요예측을 위한 신경회로망 시스템의 개발)

  • Kim, Kwang-Ho;Youn, Hyoung-Sun
    • Proceedings of the KIEE Conference
    • /
    • 1998.07c
    • /
    • pp.850-853
    • /
    • 1998
  • This paper proposes a new short-term load forecasting method for special day, such as Public holidays, consecutive holidays, and days before and after holidays. when the load curves are quite different from those of normal weekdays. In this paper, two Artificial Neural Network(ANN) systems are applied to short-term load forecasting for spacial days in anomalous load conditions.

  • PDF

Various Models of Fuzzy Least-Squares Linear Regression for Load Forecasting (전력수요예측을 위한 다양한 퍼지 최소자승 선형회귀 모델)

  • Song, Kyung-Bin
    • Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
    • /
    • v.21 no.7
    • /
    • pp.61-67
    • /
    • 2007
  • The load forecasting has been an important part of power system Accordingly, it has been proposed various methods for the load forecasting. The load patterns of the special days is quite different than those of ordinary weekdays. It is difficult to accurately forecast the load of special days due to the insufficiency of the load patterns compared with ordinary weekdays, so we have proposed fuzzy least squares linear regression algorithm for the load forecasting. In this paper we proposed four models for fuzzy least squares linear regression. It is separated by coefficients of fuzzy least squares linear regression equation. we compared model of H1 with H4 and prove it H4 has accurately forecast better than H1.

Development of a Weekly Load Forecasting Expert System (주간수요예측 전문가 시스템 개발)

  • Hwang, Kap-Ju;Kim, Kwang-Ho;Kim, Sung-Hak
    • The Transactions of the Korean Institute of Electrical Engineers A
    • /
    • v.48 no.4
    • /
    • pp.365-370
    • /
    • 1999
  • This paper describes the Weekly Load Forecasting Expert System(Named WLoFy) which was developed and implemented for Korea Electric Power Corporation(KEPCO). WLoFy was designed to provide user oriented features with a graphical user interface to improve the user interaction. The various forecasting models such as exponential smoothing, multiple regression, artificial nerual networks, rult-based model, and relative coefficient model also have been included in WLofy to increase the forecasting accuracy. The simulation based on historical data shows that the weekly forecasting results form WLoFy is an improvement when compared to the results from the conventional methods. Especially the forecasting accuracy on special days has been improved remakably.

  • PDF