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최대수요전력 관리 장치의 부하 예측에 관한 연구

A Study on the Load Forecasting Methods of Peak Electricity Demand Controller

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초록

Demand Controller is a load control device that monitor the current power consumption and calculate the forecast power to not exceed the power set by consumer. Accurate demand forecasting is important because of controlling the load use the way that sound a warning and then blocking the load when if forecasted demand exceed the power set by consumer. When if consumer with fluctuating power consumption use the existing forecasting method, management of demand control has the disadvantage of not stable. In this paper, load forecasting of the unit of seconds using the Exponential Smoothing Methods, ARIMA model, Kalman Filter is proposed. Also simulation of load forecasting of the unit of the seconds methods and existing forecasting methods is performed and analyzed the accuracy. As a result of simulation, the accuracy of load forecasting methods in seconds is higher

Abstract

Demand Controller is a load control device that monitor the current power consumption and calculate the forecast power to not exceed the power set by consumer. Accurate demand forecasting is important because of controlling the load use the way that sound a warning and then blocking the load when if forecasted demand exceed the power set by consumer. When if consumer with fluctuating power consumption use the existing forecasting method, management of demand control has the disadvantage of not stable. In this paper, load forecasting of the unit of seconds using the Exponential Smoothing Methods, ARIMA model, Kalman Filter is proposed. Also simulation of load forecasting of the unit of the seconds methods and existing forecasting methods is performed and analyzed the accuracy. As a result of simulation, the accuracy of load forecasting methods in seconds is higher

참고문헌 (10)

  1. 최인호, "스마트 그리드를 위한 무선 기반의 수요 반응 기능을 가지는 조명 제어용 장치 개발 및 실험적 평가", 전기학회논문지, 60(6), pp.1215~1224 (2011), 대한전기학회
  2. "For 2 Days 'Caution' Stage …Emergency Warning of Power Supply and Demand" (2012)[URL]
  3. "Korea Power Exchange"[URL]
  4. B.J. Kim, "Development of Intelligent Demand Controller", Journal of the Korean Institute of Illuminating and Electical Installation Engineers, 14(5), pp.50~55 (2000)
  5. C.I. Kim, "A Study on the Demand Forecasting Control using A Composite Fuzzy Model", The Transactions of the Korean Institute of Electrical Engineers, 51(9), pp.417~424 (2002)
  6. S.W. Cho, "A Study on the Control System of Maximum Demand Power Using Neural Network and Fuzzy Logic", Journal of Korean Institute of Intelligent Systems, 9(4), pp.420~420 (1999)
  7. W.W. Lee, "Statistical Technique for Prediction", Jayu Academy Books, 35~176 (2011)
  8. J.D. Lee, "A Study on Path Travel Time Prediction using Kalman Filter", 석사, pp.2004
  9. H.J. Kim, "A Study on Travel Time Prediction of the Interrupted Traffic Flow using Kalman Filter Algorithm", Journal of Yooshin Engineering Corporation, (18), pp.78~89 (2012)
  10. S.P. Kim, "Essential Kalman Filter", pp.39~112 (2010), A-Jin Publishing Company

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